add pack op for aicpu

mindspore/ccsrc/kernel/aicpu/aicpu_kernel_metadata.cc

@@ -38,10 +38,10 @@ void AicpuMetadataInfo(const CNodePtr &kernel_node, std::vector<std::shared_ptr<
     return;
   }
   // For compatibility with the current framework
-  if (op_name == kPrint || op_name == kGetNext) {
+  if (op_name == kPrint || op_name == kGetNext || op_name == kPack) {
     std::vector<std::string> inputs_format{};
     std::vector<TypeId> inputs_type{};
-    if (op_name == kPrint) {
+    if (op_name == kPrint || op_name == kPack) {
       for (size_t input_index = 0; input_index < AnfAlgo::GetInputTensorNum(kernel_node); ++input_index) {
         inputs_format.emplace_back(kOpFormat_DEFAULT);
         inputs_type.push_back(AnfAlgo::GetPrevNodeOutputInferDataType(kernel_node, input_index));

mindspore/ccsrc/kernel/aicpu/aicpu_util.h

@@ -28,6 +28,7 @@ constexpr auto kInitDataSetQueue = "InitDataSetQueue";
 constexpr auto kInitData = "InitData";
 constexpr auto kGetNext = "GetNext";
 constexpr auto kPrint = "Print";
+constexpr auto kPack = "Pack";
 constexpr auto kOutputTypes = "output_types";
 constexpr auto kOutputShapes = "output_shapes";
 constexpr auto kChannelName = "channel_name";

mindspore/ops/_op_impl/aicpu/__init__.py

@@ -24,3 +24,4 @@ from .flatten import _flatten_aicpu
 from .squeeze import _squeeze_aicpu
 from .expand_dims import _expand_dims_aicpu
 from .random_choice_with_mask import _random_choice_with_mask_aicpu
+from .pack import _pack_aicpu

mindspore/ops/_op_impl/aicpu/pack.py

@@ -0,0 +1,41 @@
+# 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.
+# ============================================================================
+
+"""Pack op"""
+from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType
+
+pack_op_info = AiCPURegOp("Pack") \
+    .fusion_type("OPAQUE") \
+    .attr("axis", "int") \
+    .input(0, "x", "dynamic") \
+    .output(0, "y", "required") \
+    .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) \
+    .get_op_info()
+
+@op_info_register(pack_op_info)
+def _pack_aicpu():
+    """Pack AiCPU register"""
+    return

mindspore/ops/_op_impl/aicpu/topk.py

@@ -24,6 +24,7 @@ top_k_op_info = AiCPURegOp("TopK") \
     .output(0, "values", "required") \
     .output(1, "indices", "required") \
     .dtype_format(DataType.F16_Default, DataType.I32_Default, DataType.F16_Default, DataType.I32_Default) \
+    .dtype_format(DataType.F32_Default, DataType.I32_Default, DataType.F32_Default, DataType.I32_Default) \
     .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
     .get_op_info()
 

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

@@ -0,0 +1,176 @@
+# 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.
+# ============================================================================
+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
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+
+class Net(nn.Cell):
+    def __init__(self, x, axis):
+        super(Net, self).__init__()
+        self.pack = P.Pack(axis)
+        self.x = x
+
+    def construct(self):
+        return self.pack(self.x)
+
+
+def test_net_bool():
+    x = np.random.randn(3, 5, 4) > 0
+    y = np.random.randn(3, 5, 4) > 0
+    axis = -1
+    net = Net((Tensor(x), Tensor(y)), axis)
+    output = net()
+    print(x)
+    print(y)
+    print(output.asnumpy())
+    assert np.array_equal(output.asnumpy(), np.stack([x, y], axis))
+
+
+def test_net_int8():
+    x = np.random.randn(3, 5, 4).astype(np.int8)
+    y = np.random.randn(3, 5, 4).astype(np.int8)
+    axis = -1
+    net = Net((Tensor(x), Tensor(y)), axis)
+    output = net()
+    print(x)
+    print(y)
+    print(output.asnumpy())
+    assert np.array_equal(output.asnumpy(), np.stack([x, y], axis))
+
+
+def test_net_uint8():
+    x = np.random.randn(3, 5, 4).astype(np.uint8)
+    y = np.random.randn(3, 5, 4).astype(np.uint8)
+    axis = -1
+    net = Net((Tensor(x), Tensor(y)), axis)
+    output = net()
+    print(x)
+    print(y)
+    print(output.asnumpy())
+    assert np.array_equal(output.asnumpy(), np.stack([x, y], axis))
+
+
+def test_net_int16():
+    x = np.random.randn(3, 5, 4).astype(np.int16)
+    y = np.random.randn(3, 5, 4).astype(np.int16)
+    axis = -1
+    net = Net((Tensor(x), Tensor(y)), axis)
+    output = net()
+    print(x)
+    print(y)
+    print(output.asnumpy())
+    assert np.array_equal(output.asnumpy(), np.stack([x, y], axis))
+
+
+def test_net_uint16():
+    x = np.random.randn(3, 5, 4).astype(np.uint16)
+    y = np.random.randn(3, 5, 4).astype(np.uint16)
+    axis = -1
+    net = Net((Tensor(x), Tensor(y)), axis)
+    output = net()
+    print(x)
+    print(y)
+    print(output.asnumpy())
+    assert np.array_equal(output.asnumpy(), np.stack([x, y], axis))
+
+
+def test_net_int32():
+    x = np.random.randn(3, 5, 4).astype(np.int32)
+    y = np.random.randn(3, 5, 4).astype(np.int32)
+    axis = -1
+    net = Net((Tensor(x), Tensor(y)), axis)
+    output = net()
+    print(x)
+    print(y)
+    print(output.asnumpy())
+    assert np.array_equal(output.asnumpy(), np.stack([x, y], axis))
+
+
+def test_net_uint32():
+    x = np.random.randn(3, 5, 4).astype(np.uint32)
+    y = np.random.randn(3, 5, 4).astype(np.uint32)
+    axis = -1
+    net = Net((Tensor(x), Tensor(y)), axis)
+    output = net()
+    print(x)
+    print(y)
+    print(output.asnumpy())
+    assert np.array_equal(output.asnumpy(), np.stack([x, y], axis))
+
+
+def test_net_int64():
+    x = np.random.randn(3, 5, 4).astype(np.int64)
+    y = np.random.randn(3, 5, 4).astype(np.int64)
+    axis = -1
+    net = Net((Tensor(x), Tensor(y)), axis)
+    output = net()
+    print(x)
+    print(y)
+    print(output.asnumpy())
+    assert np.array_equal(output.asnumpy(), np.stack([x, y], axis))
+
+
+def test_net_uint64():
+    x = np.random.randn(3, 5, 4).astype(np.uint64)
+    y = np.random.randn(3, 5, 4).astype(np.uint64)
+    axis = -1
+    net = Net((Tensor(x), Tensor(y)), axis)
+    output = net()
+    print(x)
+    print(y)
+    print(output.asnumpy())
+    assert np.array_equal(output.asnumpy(), np.stack([x, y], axis))
+
+
+def test_net_float16():
+    x = np.random.randn(3, 5, 4).astype(np.float16)
+    y = np.random.randn(3, 5, 4).astype(np.float16)
+    axis = -1
+    net = Net((Tensor(x), Tensor(y)), axis)
+    output = net()
+    print(x)
+    print(y)
+    print(output.asnumpy())
+    assert np.array_equal(output.asnumpy(), np.stack([x, y], axis))
+
+
+def test_net_float32():
+    x = np.random.randn(3, 5, 4).astype(np.float32)
+    y = np.random.randn(3, 5, 4).astype(np.float32)
+    axis = -1
+    net = Net((Tensor(x), Tensor(y)), axis)
+    output = net()
+    print(x)
+    print(y)
+    print(output.asnumpy())
+    assert np.array_equal(output.asnumpy(), np.stack([x, y], axis))
+
+
+def test_net_float64():
+    x = np.random.randn(3, 5, 4).astype(np.float64)
+    y = np.random.randn(3, 5, 4).astype(np.float64)
+    axis = -1
+    net = Net((Tensor(x), Tensor(y)), axis)
+    output = net()
+    print(x)
+    print(y)
+    print(output.asnumpy())
+    assert np.array_equal(output.asnumpy(), np.stack([x, y], axis))