!2014 adapt op SparseGatherV2

Merge pull request !2014 from zhaozhenlong/op/sparse-gather-v2-vm

mindspore/ccsrc/kernel/tbe/tbe_adapter.cc

@@ -53,6 +53,7 @@ static std::map<string, string> tbe_func_adapter_map = {
   {"scatter_nd", "scatter_nd_d"},
   {"tile", "tile_d"},
   {"gather_v2", "gather_v2_d"},
+  {"sparse_gather_v2", "gather_v2_d"},
   {"batch_mat_mul", "batch_matmul"},
   {"b_n_training_reduce", "bn_training_reduce"},
   {"b_n_training_update", "bn_training_update"},

mindspore/ccsrc/pre_activate/pass/const_input_to_attr_registry.cc

@@ -47,6 +47,7 @@ ConstInputToAttrInfoRegistry::ConstInputToAttrInfoRegistry() {
   Register(prim::kPrimCumProd->name(), {1});
   Register(prim::kPrimReduceAll->name(), {1});
   Register(prim::kPrimUnsortedSegmentMin->name(), {2});
+  Register(kSparseGatherV2, {2});
   Register(kUnsortedSegmentProdOpName, {2});
   Register(kSimpleMeanGradOpName, {1});
   Register(kMeanGradOpName, {1});

mindspore/ccsrc/utils/utils.h

@@ -65,6 +65,7 @@ constexpr auto kScatterNdOpName = "ScatterNd";
 constexpr auto kStridedSliceAssignOpName = "StridedSliceAssign";
 constexpr auto kStridedSliceOpName = "StridedSlice";
 constexpr auto kStridedSliceGradOpName = "StridedSliceGrad";
+constexpr auto kSparseGatherV2 = "SparseGatherV2";
 constexpr auto kUnsortedSegmentProdOpName = "UnsortedSegmentProd";
 constexpr auto kUnsortedSegmentMinOpName = "UnsortedSegmentMin";
 constexpr auto kFlattenGradOpName = "FlattenGrad";

mindspore/ops/_op_impl/tbe/__init__.py

@@ -248,3 +248,4 @@ from .range import _range_tbe
 from .fused_mul_add_n_l2loss import _fused_mul_add_n_l2loss_tbe
 from .fused_mul_apply_momentum_extern import _fused_mul_apply_momentum_extern_tbe
 from .lamb_next_right import _lamb_next_right_tbe
+from .sparse_gather_v2 import _sparse_gather_v2_tbe

mindspore/ops/_op_impl/tbe/sparse_gather_v2.py

+# 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.
+# ============================================================================
+
+"""SparseGatherV2 op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+sparse_gather_v2_op_info = TBERegOp("SparseGatherV2") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("gather_v2_d.so") \
+    .compute_cost(10) \
+    .kernel_name("gather_v2_d") \
+    .partial_flag(True) \
+    .attr("axis", "optional", "int", "all") \
+    .input(0, "x", False, "required", "all") \
+    .input(1, "indices", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I8_Default, DataType.I32_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I8_Default, DataType.I64_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I8_5HD, DataType.I32_5HD, DataType.I8_5HD) \
+    .dtype_format(DataType.I8_5HD, DataType.I64_5HD, DataType.I8_5HD) \
+    .dtype_format(DataType.I8_FracZ, DataType.I32_FracZ, DataType.I8_FracZ) \
+    .dtype_format(DataType.I8_FracZ, DataType.I64_FracZ, DataType.I8_FracZ) \
+    .dtype_format(DataType.U8_Default, DataType.I32_Default, DataType.U8_Default) \
+    .dtype_format(DataType.U8_Default, DataType.I64_Default, DataType.U8_Default) \
+    .dtype_format(DataType.U8_5HD, DataType.I32_5HD, DataType.U8_5HD) \
+    .dtype_format(DataType.U8_5HD, DataType.I64_5HD, DataType.U8_5HD) \
+    .dtype_format(DataType.U8_FracZ, DataType.I32_FracZ, DataType.U8_FracZ) \
+    .dtype_format(DataType.U8_FracZ, DataType.I64_FracZ, DataType.U8_FracZ) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I64_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I32_5HD, DataType.I32_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.I32_5HD, DataType.I64_5HD, DataType.I32_5HD) \
+    .dtype_format(DataType.I32_FracZ, DataType.I32_FracZ, DataType.I32_FracZ) \
+    .dtype_format(DataType.I32_FracZ, DataType.I64_FracZ, DataType.I32_FracZ) \
+    .dtype_format(DataType.F16_Default, DataType.I32_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_Default, DataType.I64_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F16_5HD, DataType.I32_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_5HD, DataType.I64_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F16_FracZ, DataType.I32_FracZ, DataType.F16_FracZ) \
+    .dtype_format(DataType.F16_FracZ, DataType.I64_FracZ, DataType.F16_FracZ) \
+    .dtype_format(DataType.F32_Default, DataType.I32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_Default, DataType.I64_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F32_5HD, DataType.I32_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_5HD, DataType.I64_5HD, DataType.F32_5HD) \
+    .dtype_format(DataType.F32_FracZ, DataType.I32_FracZ, DataType.F32_FracZ) \
+    .dtype_format(DataType.F32_FracZ, DataType.I64_FracZ, DataType.F32_FracZ) \
+    .get_op_info()
+
+
+@op_info_register(sparse_gather_v2_op_info)
+def _sparse_gather_v2_tbe():
+    """SparseGatherV2 TBE register"""
+    return

tests/ut/python/ops/test_ops.py

@@ -956,6 +956,11 @@ test_case_nn_ops = [
         'desc_const': [0],
         'desc_inputs': [[1152], Tensor(np.array(10).astype(np.int32))],
         'desc_bprop': [Tensor(np.array(10).astype(np.float32))]}),
+    ('SparseGatherV2_0', {
+        'block': P.SparseGatherV2(),
+        'desc_const': [0],
+        'desc_inputs': [[3, 1, 2], Tensor(np.array([0, 1]).astype(np.int32))],
+        'desc_bprop': [[2, 1, 2]]}),
     ('Range', {
         'block': P.Range(1.0, 5.0),
         'desc_inputs': [Tensor(np.ones([10]).astype(np.float32))],