add aicpu ops of Reshape/Flatten/Squeeze/ExpandDims/IsFinite

mindspore/ccsrc/kernel/aicpu/aicpu_util.h

@@ -27,7 +27,6 @@ namespace kernel {
 constexpr auto kInitDataSetQueue = "InitDataSetQueue";
 constexpr auto kInitData = "InitData";
 constexpr auto kGetNext = "GetNext";
-constexpr auto kDropoutGenMask = "DropoutGenMask";
 constexpr auto kPrint = "Print";
 
 constexpr auto kOutputTypes = "output_types";

mindspore/ccsrc/pre_activate/common/helper.cc

@@ -340,8 +340,23 @@ bool IsNopNode(const AnfNodePtr &node) {
   return true;
 }
 
+bool IsAllNopNode(session::KernelGraph *const graph) {
+  MS_EXCEPTION_IF_NULL(graph);
+  auto execution_order = graph->execution_order();
+  for (auto &cnode : execution_order) {
+    MS_EXCEPTION_IF_NULL(cnode);
+    if (!IsNopNode(cnode)) {
+      return false;
+    }
+  }
+  return true;
+}
+
 void HideNopNode(session::KernelGraph *const graph) {
   MS_EXCEPTION_IF_NULL(graph);
+  if (IsAllNopNode(graph) == true) {
+    return;
+  }
   auto execution_order = graph->execution_order();
   MS_LOG(INFO) << "nop node info (Before Remove) size: " << execution_order.size();
   std::vector<CNodePtr> new_nodes;
@@ -357,6 +372,9 @@ void HideNopNode(session::KernelGraph *const graph) {
 
 void RemoveNopNode(session::KernelGraph *const graph) {
   MS_EXCEPTION_IF_NULL(graph);
+  if (IsAllNopNode(graph) == true) {
+    return;
+  }
   bool changed = true;
   while (changed) {
     changed = false;

mindspore/ops/_op_impl/aicpu/__init__.py

@@ -17,3 +17,8 @@ from .init_data_set_queue import _init_data_set_queue_aicpu
 from .dropout_genmask import _dropout_genmask_aicpu
 from .get_next import _get_next_aicpu
 from .print_tensor import _print_aicpu
+from .is_finite import _is_finite_aicpu
+from .reshape import _reshape_aicpu
+from .flatten import _flatten_aicpu
+from .squeeze import _squeeze_aicpu
+from .expand_dims import _expand_dims_aicpu

mindspore/ops/_op_impl/aicpu/expand_dims.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.
+# ============================================================================
+
+"""ExpandDims op"""
+from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType
+
+expand_dims_op_info = AiCPURegOp("ExpandDims") \
+    .fusion_type("OPAQUE") \
+    .input(0, "x", "required") \
+    .output(0, "y", "required") \
+    .dtype_format(DataType.BOOL_Default, DataType.BOOL_Default) \
+    .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_NCHW, DataType.BOOL_NCHW) \
+    .dtype_format(DataType.I8_NCHW, DataType.I8_NCHW) \
+    .dtype_format(DataType.I16_NCHW, DataType.I16_NCHW) \
+    .dtype_format(DataType.I32_NCHW, DataType.I32_NCHW) \
+    .dtype_format(DataType.I64_NCHW, DataType.I64_NCHW) \
+    .dtype_format(DataType.U8_NCHW, DataType.U8_NCHW) \
+    .dtype_format(DataType.U16_NCHW, DataType.U16_NCHW) \
+    .dtype_format(DataType.U32_NCHW, DataType.U32_NCHW) \
+    .dtype_format(DataType.U64_NCHW, DataType.U64_NCHW) \
+    .dtype_format(DataType.F16_NCHW, DataType.F16_NCHW) \
+    .dtype_format(DataType.F32_NCHW, DataType.F32_NCHW) \
+    .dtype_format(DataType.F64_NCHW, DataType.F64_NCHW) \
+    .get_op_info()
+
+@op_info_register(expand_dims_op_info)
+def _expand_dims_aicpu():
+    """ExpandDims AiCPU register"""
+    return

mindspore/ops/_op_impl/aicpu/flatten.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.
+# ============================================================================
+
+"""Flatten op"""
+from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType
+
+flatten_op_info = AiCPURegOp("Flatten") \
+    .fusion_type("OPAQUE") \
+    .input(0, "x", "required") \
+    .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.I8_NCHW, DataType.I8_NCHW) \
+    .dtype_format(DataType.I16_NCHW, DataType.I16_NCHW) \
+    .dtype_format(DataType.I32_NCHW, DataType.I32_NCHW) \
+    .dtype_format(DataType.I64_NCHW, DataType.I64_NCHW) \
+    .dtype_format(DataType.U8_NCHW, DataType.U8_NCHW) \
+    .dtype_format(DataType.U16_NCHW, DataType.U16_NCHW) \
+    .dtype_format(DataType.U32_NCHW, DataType.U32_NCHW) \
+    .dtype_format(DataType.U64_NCHW, DataType.U64_NCHW) \
+    .dtype_format(DataType.F16_NCHW, DataType.F16_NCHW) \
+    .dtype_format(DataType.F32_NCHW, DataType.F32_NCHW) \
+    .get_op_info()
+
+@op_info_register(flatten_op_info)
+def _flatten_aicpu():
+    """Flatten AiCPU register"""
+    return

mindspore/ops/_op_impl/aicpu/is_finite.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.
+# ============================================================================
+
+"""IsFinite op"""
+from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType
+
+is_finite_op_info = AiCPURegOp("IsFinite") \
+    .fusion_type("OPAQUE") \
+    .input(0, "x", "required") \
+    .output(0, "y", "required") \
+    .dtype_format(DataType.BOOL_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.I8_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.I16_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.I32_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.I64_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.U8_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.U16_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.U32_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.U64_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.F16_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.F32_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.F64_Default, DataType.BOOL_Default) \
+    .dtype_format(DataType.BOOL_NCHW, DataType.BOOL_NCHW) \
+    .dtype_format(DataType.I8_NCHW, DataType.BOOL_NCHW) \
+    .dtype_format(DataType.I16_NCHW, DataType.BOOL_NCHW) \
+    .dtype_format(DataType.I32_NCHW, DataType.BOOL_NCHW) \
+    .dtype_format(DataType.I64_NCHW, DataType.BOOL_NCHW) \
+    .dtype_format(DataType.U8_NCHW, DataType.BOOL_NCHW) \
+    .dtype_format(DataType.U16_NCHW, DataType.BOOL_NCHW) \
+    .dtype_format(DataType.U32_NCHW, DataType.BOOL_NCHW) \
+    .dtype_format(DataType.U64_NCHW, DataType.BOOL_NCHW) \
+    .dtype_format(DataType.F16_NCHW, DataType.BOOL_NCHW) \
+    .dtype_format(DataType.F32_NCHW, DataType.BOOL_NCHW) \
+    .dtype_format(DataType.F64_NCHW, DataType.BOOL_NCHW) \
+    .get_op_info()
+
+@op_info_register(is_finite_op_info)
+def _is_finite_aicpu():
+    """IsFinite AiCPU register"""
+    return

mindspore/ops/_op_impl/aicpu/reshape.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.
+# ============================================================================
+
+"""Reshape op"""
+from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType
+
+reshape_op_info = AiCPURegOp("Reshape") \
+    .fusion_type("OPAQUE") \
+    .input(0, "x", "required") \
+    .output(0, "y", "required") \
+    .dtype_format(DataType.BOOL_Default, DataType.BOOL_Default) \
+    .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_NCHW, DataType.BOOL_NCHW) \
+    .dtype_format(DataType.I8_NCHW, DataType.I8_NCHW) \
+    .dtype_format(DataType.I16_NCHW, DataType.I16_NCHW) \
+    .dtype_format(DataType.I32_NCHW, DataType.I32_NCHW) \
+    .dtype_format(DataType.I64_NCHW, DataType.I64_NCHW) \
+    .dtype_format(DataType.U8_NCHW, DataType.U8_NCHW) \
+    .dtype_format(DataType.U16_NCHW, DataType.U16_NCHW) \
+    .dtype_format(DataType.U32_NCHW, DataType.U32_NCHW) \
+    .dtype_format(DataType.U64_NCHW, DataType.U64_NCHW) \
+    .dtype_format(DataType.F16_NCHW, DataType.F16_NCHW) \
+    .dtype_format(DataType.F32_NCHW, DataType.F32_NCHW) \
+    .dtype_format(DataType.F64_NCHW, DataType.F64_NCHW) \
+    .get_op_info()
+
+@op_info_register(reshape_op_info)
+def _reshape_aicpu():
+    """Rpeshape AiCPU register"""
+    return

mindspore/ops/_op_impl/aicpu/squeeze.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.
+# ============================================================================
+
+"""Squeeze op"""
+from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType
+
+squeeze_op_info = AiCPURegOp("Squeeze") \
+    .fusion_type("OPAQUE") \
+    .input(0, "x", "required") \
+    .output(0, "y", "required") \
+    .dtype_format(DataType.BOOL_Default, DataType.BOOL_Default) \
+    .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_NCHW, DataType.BOOL_NCHW) \
+    .dtype_format(DataType.I8_NCHW, DataType.I8_NCHW) \
+    .dtype_format(DataType.I16_NCHW, DataType.I16_NCHW) \
+    .dtype_format(DataType.I32_NCHW, DataType.I32_NCHW) \
+    .dtype_format(DataType.I64_NCHW, DataType.I64_NCHW) \
+    .dtype_format(DataType.U8_NCHW, DataType.U8_NCHW) \
+    .dtype_format(DataType.U16_NCHW, DataType.U16_NCHW) \
+    .dtype_format(DataType.U32_NCHW, DataType.U32_NCHW) \
+    .dtype_format(DataType.U64_NCHW, DataType.U64_NCHW) \
+    .dtype_format(DataType.F16_NCHW, DataType.F16_NCHW) \
+    .dtype_format(DataType.F32_NCHW, DataType.F32_NCHW) \
+    .dtype_format(DataType.F64_NCHW, DataType.F64_NCHW) \
+    .get_op_info()
+
+@op_info_register(squeeze_op_info)
+def _squeeze_aicpu():
+    """Squeeze AiCPU register"""
+    return

mindspore/ops/_op_impl/tbe/__init__.py

@@ -61,9 +61,6 @@ from .reduce_mean_d import _reduce_mean_d_tbe
 from .scatter_nd import _scatter_nd_tbe
 from .scatter_nd_d import _scatter_nd_d_tbe
 from .reduce_mean import _reduce_mean_tbe
-from .reshape import _reshape_tbe
-from .expand_dims import _expand_dims_tbe
-from .squeeze import _squeeze_tbe
 from .tile import _tile_tbe
 from .atomic_addr_clean import _atomic_addr_clean_tbe
 from .gather_v2 import _gather_v2_tbe

mindspore/ops/op_info_register.py

@@ -599,4 +599,13 @@ class DataType:
     F32_NCHW = ("float32", "NCHW")
     F32_NHWC = ("float32", "NHWC")
     F32_HWCN = ("float32", "HWCN")
- 
+
\ No newline at end of file
+    F64_None = ("float64", "")
+    F64_Default = ("float64", "DefaultFormat")
+    F64_5HD = ("float64", "NC1HWC0")
+    F64_FracZ = ("float64", "FracZ")
+    F64_FracNZ = ("float64", "FRACTAL_NZ")
+    F64_C1HWNCoC0 = ("float64", "C1HWNCoC0")
+    F64_NCHW = ("float64", "NCHW")
+    F64_NHWC = ("float64", "NHWC")
+    F64_HWCN = ("float64", "HWCN")

tests/st/ops/davinci/test_aicpu_ops/test_expand_dims.py

+# Copyright 2019 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.
+# ============================================================================
+from mindspore import Tensor
+from mindspore.ops import operations as P
+import mindspore.nn as nn
+from mindspore.common.api import ms_function
+import numpy as np
+import mindspore.context as context
+context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
+class Net(nn.Cell):
+  def __init__(self):
+    super(Net, self).__init__()
+    self.expand_dims = P.ExpandDims()
+
+  def construct(self, tensor, dim):
+    return self.expand_dims(tensor, dim)
+
+
+def test_net_bool():
+  x = np.random.randn(1, 16, 1, 1).astype(np.bool)
+  net = Net()
+  output = net(Tensor(x), -1)
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == np.expand_dims(x, -1)))
+
+def test_net_int8():
+  x = np.random.randn(1, 16, 1, 1).astype(np.int8)
+  net = Net()
+  output = net(Tensor(x), -1)
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == np.expand_dims(x, -1)))
+  
+def test_net_uint8():
+  x = np.random.randn(1, 16, 1, 1).astype(np.uint8)
+  net = Net()
+  output = net(Tensor(x), -1)
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == np.expand_dims(x, -1)))
+
+def test_net_int16():
+  x = np.random.randn(1, 16, 1, 1).astype(np.int16)
+  net = Net()
+  output = net(Tensor(x), -1)
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == np.expand_dims(x, -1)))
+
+def test_net_uint16():
+  x = np.random.randn(1, 16, 1, 1).astype(np.uint16)
+  net = Net()
+  output = net(Tensor(x), -1)
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == np.expand_dims(x, -1)))
+
+def test_net_int32():
+  x = np.random.randn(1, 16, 1, 1).astype(np.int32)
+  net = Net()
+  output = net(Tensor(x), -1)
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == np.expand_dims(x, -1)))
+
+def test_net_uint32():
+  x = np.random.randn(1, 16, 1, 1).astype(np.uint32)
+  net = Net()
+  output = net(Tensor(x), -1)
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == np.expand_dims(x, -1)))
+
+def test_net_int64():
+  x = np.random.randn(1, 16, 1, 1).astype(np.int64)
+  net = Net()
+  output = net(Tensor(x), -1)
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == np.expand_dims(x, -1)))
+
+def test_net_uint64():
+  x = np.random.randn(1, 16, 1, 1).astype(np.uint64)
+  net = Net()
+  output = net(Tensor(x), -1)
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == np.expand_dims(x, -1)))
+
+def test_net_float16():
+  x = np.random.randn(1, 16, 1, 1).astype(np.float16)
+  net = Net()
+  output = net(Tensor(x), -1)
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == np.expand_dims(x, -1)))
+
+def test_net_float32():
+  x = np.random.randn(1, 16, 1, 1).astype(np.float32)
+  net = Net()
+  output = net(Tensor(x), -1)
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == np.expand_dims(x, -1)))
+
+def test_net_float64():
+  x = np.random.randn(1, 16, 1, 1).astype(np.float64)
+  net = Net()
+  output = net(Tensor(x), -1)
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == np.expand_dims(x, -1)))
+  

tests/st/ops/davinci/test_aicpu_ops/test_flatten.py

+# Copyright 2019 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.
+# ============================================================================
+from mindspore import Tensor
+from mindspore.ops import operations as P
+import mindspore.nn as nn
+import numpy as np
+import mindspore.context as context
+context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
+class Net(nn.Cell):
+  def __init__(self):
+    super(Net, self).__init__()
+    self.flatten = P.Flatten()
+
+  def construct(self, tensor):
+    return self.flatten(tensor)
+
+
+def test_net_int8():
+  x = np.random.randn(1, 16, 1, 1).astype(np.int8)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == x.flatten()))
+  
+def test_net_uint8():
+  x = np.random.randn(1, 16, 1, 1).astype(np.uint8)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == x.flatten()))
+
+def test_net_int16():
+  x = np.random.randn(1, 16, 1, 1).astype(np.int16)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == x.flatten()))
+
+def test_net_uint16():
+  x = np.random.randn(1, 16, 1, 1).astype(np.uint16)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == x.flatten()))
+
+def test_net_int32():
+  x = np.random.randn(1, 16, 1, 1).astype(np.int32)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == x.flatten()))
+
+def test_net_uint32():
+  x = np.random.randn(1, 16, 1, 1).astype(np.uint32)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == x.flatten()))
+
+def test_net_int64():
+  x = np.random.randn(1, 16, 1, 1).astype(np.int64)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == x.flatten()))
+
+def test_net_uint64():
+  x = np.random.randn(1, 16, 1, 1).astype(np.uint64)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == x.flatten()))
+
+def test_net_float16():
+  x = np.random.randn(1, 16, 1, 1).astype(np.float16)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == x.flatten()))
+
+def test_net_float32():
+  x = np.random.randn(1, 16, 1, 1).astype(np.float32)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == x.flatten()))
+  

tests/st/ops/davinci/test_aicpu_ops/test_is_finite.py

+# Copyright 2019 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.
+# ============================================================================
+from mindspore import Tensor
+from mindspore.ops import operations as P
+import mindspore.nn as nn
+from mindspore.common.api import ms_function
+import numpy as np
+import mindspore.context as context
+context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
+class Net(nn.Cell):
+  def __init__(self):
+    super(Net, self).__init__()
+    self.isfinite = P.IsFinite()
+
+  def construct(self, tensor):
+    return self.isfinite(tensor)
+
+
+def test_net_bool():
+  x = np.random.randn(1, 16, 1, 1).astype(np.bool)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == np.isfinite(x)))
+
+def test_net_int8():
+  x = np.random.randn(1, 16, 1, 1).astype(np.int8)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == np.isfinite(x)))
+  
+def test_net_uint8():
+  x = np.random.randn(1, 16, 1, 1).astype(np.uint8)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == np.isfinite(x)))
+
+def test_net_int16():
+  x = np.random.randn(1, 16, 1, 1).astype(np.int16)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == np.isfinite(x)))
+
+def test_net_uint16():
+  x = np.random.randn(1, 16, 1, 1).astype(np.uint16)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == np.isfinite(x)))
+
+def test_net_int32():
+  x = np.random.randn(1, 16, 1, 1).astype(np.int32)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == np.isfinite(x)))
+
+def test_net_uint32():
+  x = np.random.randn(1, 16, 1, 1).astype(np.uint32)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == np.isfinite(x)))
+
+def test_net_int64():
+  x = np.random.randn(1, 16, 1, 1).astype(np.int64)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == np.isfinite(x)))
+
+def test_net_uint64():
+  x = np.random.randn(1, 16, 1, 1).astype(np.uint64)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == np.isfinite(x)))
+
+def test_net_float16():
+  x = np.random.randn(1, 16, 1, 1).astype(np.float16)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == np.isfinite(x)))
+
+def test_net_float32():
+  x = np.random.randn(1, 16, 1, 1).astype(np.float32)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == np.isfinite(x)))
+
+def test_net_float64():
+  x = np.random.randn(1, 16, 1, 1).astype(np.float64)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == np.isfinite(x)))
+  

tests/st/ops/davinci/test_aicpu_ops/test_reshape.py

+# Copyright 2019 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.
+# ============================================================================
+from mindspore import Tensor
+from mindspore.ops import operations as P
+import mindspore.nn as nn
+from mindspore.common.api import ms_function
+import numpy as np
+import mindspore.context as context
+context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
+class Net(nn.Cell):
+  def __init__(self):
+    super(Net, self).__init__()
+    self.reshape = P.Reshape()
+
+  def construct(self, tensor):
+    return self.reshape(tensor, (4,4))
+
+
+def test_net_bool():
+  x = np.random.randn(1, 16, 1, 1).astype(np.bool)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == np.reshape(x, (4,4))))
+
+def test_net_int8():
+  x = np.random.randn(1, 16, 1, 1).astype(np.int8)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == np.reshape(x, (4,4))))
+  
+def test_net_uint8():
+  x = np.random.randn(1, 16, 1, 1).astype(np.uint8)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == np.reshape(x, (4,4))))
+
+def test_net_int16():
+  x = np.random.randn(1, 16, 1, 1).astype(np.int16)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == np.reshape(x, (4,4))))
+
+def test_net_uint16():
+  x = np.random.randn(1, 16, 1, 1).astype(np.uint16)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == np.reshape(x, (4,4))))
+
+def test_net_int32():
+  x = np.random.randn(1, 16, 1, 1).astype(np.int32)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == np.reshape(x, (4,4))))
+
+def test_net_uint32():
+  x = np.random.randn(1, 16, 1, 1).astype(np.uint32)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == np.reshape(x, (4,4))))
+
+def test_net_int64():
+  x = np.random.randn(1, 16, 1, 1).astype(np.int64)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == np.reshape(x, (4,4))))
+
+def test_net_uint64():
+  x = np.random.randn(1, 16, 1, 1).astype(np.uint64)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == np.reshape(x, (4,4))))
+
+def test_net_float16():
+  x = np.random.randn(1, 16, 1, 1).astype(np.float16)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == np.reshape(x, (4,4))))
+
+def test_net_float32():
+  x = np.random.randn(1, 16, 1, 1).astype(np.float32)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == np.reshape(x, (4,4))))
+
+def test_net_float64():
+  x = np.random.randn(1, 16, 1, 1).astype(np.float64)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == np.reshape(x, (4,4))))
+  

tests/st/ops/davinci/test_aicpu_ops/test_squeeze.py

+# Copyright 2019 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.
+# ============================================================================
+from mindspore import Tensor
+from mindspore.ops import operations as P
+import mindspore.nn as nn
+import numpy as np
+import mindspore.context as context
+context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
+class Net(nn.Cell):
+  def __init__(self):
+    super(Net, self).__init__()
+    self.squeeze = P.Squeeze()
+
+  def construct(self, tensor):
+    return self.squeeze(tensor)
+
+
+def test_net_bool():
+  x = np.random.randn(1, 16, 1, 1).astype(np.bool)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == x.squeeze()))
+
+def test_net_int8():
+  x = np.random.randn(1, 16, 1, 1).astype(np.int8)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == x.squeeze()))
+  
+def test_net_uint8():
+  x = np.random.randn(1, 16, 1, 1).astype(np.uint8)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == x.squeeze()))
+
+def test_net_int16():
+  x = np.random.randn(1, 16, 1, 1).astype(np.int16)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == x.squeeze()))
+
+def test_net_uint16():
+  x = np.random.randn(1, 16, 1, 1).astype(np.uint16)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == x.squeeze()))
+
+def test_net_int32():
+  x = np.random.randn(1, 16, 1, 1).astype(np.int32)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == x.squeeze()))
+
+def test_net_uint32():
+  x = np.random.randn(1, 16, 1, 1).astype(np.uint32)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == x.squeeze()))
+
+def test_net_int64():
+  x = np.random.randn(1, 16, 1, 1).astype(np.int64)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == x.squeeze()))
+
+def test_net_uint64():
+  x = np.random.randn(1, 16, 1, 1).astype(np.uint64)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == x.squeeze()))
+
+def test_net_float16():
+  x = np.random.randn(1, 16, 1, 1).astype(np.float16)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == x.squeeze()))
+
+def test_net_float32():
+  x = np.random.randn(1, 16, 1, 1).astype(np.float32)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == x.squeeze()))
+
+def test_net_float64():
+  x = np.random.randn(1, 16, 1, 1).astype(np.float64)
+  net = Net()
+  output = net(Tensor(x))
+  print(output.asnumpy())
+  assert(np.all(output.asnumpy() == x.squeeze()))
+