!1194 test(custom_op): add custom op testcase.

Merge pull request !1194 from gongchen/add_custom_op_test

tests/st/ops/custom_ops_tbe/add3_impl.py

@@ -0,0 +1,62 @@
+# 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.
+# ============================================================================
+from __future__ import absolute_import
+from te import tvm
+from topi import generic
+import te.lang.cce
+from topi.cce import util
+from te.platform.fusion_manager import fusion_manager
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+@fusion_manager.register("add3")
+def add3_compute(input1, input2, const_bias):
+    sum2 = te.lang.cce.vadd(input1, input2)
+    sum3 = te.lang.cce.vadds(sum2, tvm.const(const_bias, dtype = input1.dtype))
+    return sum3
+
+
+cus_add3_op_info = TBERegOp("CusAdd3") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("add3.so") \
+    .compute_cost(10) \
+    .kernel_name("CusAdd3Impl") \
+    .partial_flag(True) \
+    .attr("const_bias", "required", "float", "all") \
+    .input(0, "input1", False, "required", "all") \
+    .input(1, "input2", False, "required", "all") \
+    .output(0, "sum", False, "required", "all") \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
+    .get_op_info()
+
+
+@op_info_register(cus_add3_op_info)
+def CusAdd3Impl(input1, inptu2, sum, const_bias, kernel_name="CusAdd3Impl"):
+    shape = input1.get("shape")
+    shape = util.shape_refine(shape)
+    dtype = input1.get("dtype").lower()
+    input1 = tvm.placeholder(shape, name="input1", dtype=dtype.lower())
+    input2 = tvm.placeholder(shape, name="input2", dtype=dtype.lower())
+
+    with tvm.target.cce():
+        res = add3_compute(input1, input2, const_bias)
+        sch = generic.auto_schedule(res)
+
+    config = {"print_ir": False,
+              "name": kernel_name,
+              "tensor_list": [input1, input2, res]}
+
+    te.lang.cce.cce_build_code(sch, config)

tests/st/ops/custom_ops_tbe/cus_add3.py

@@ -0,0 +1,32 @@
+# 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
+from mindspore.ops import prim_attr_register, PrimitiveWithInfer
+from mindspore.ops import operations as P
+from mindspore import Tensor
+
+# sum = input1 + input2 + const_bias
+class CusAdd3(PrimitiveWithInfer):
+    """Custom add3 definition"""
+    @prim_attr_register
+    def __init__(self, const_bias=0.0):
+        self.init_prim_io_names(inputs=['input1', 'input2'], outputs=['sum3'])
+        from add3_impl import CusAdd3Impl
+
+    def infer_shape(self, input1, input2):
+        return input1
+
+    def infer_dtype(self, input1, input2):
+        return input1

tests/st/ops/custom_ops_tbe/cus_square.py

@@ -13,10 +13,9 @@
 # limitations under the License.
 # ============================================================================
 import numpy as np
-
 from mindspore import Tensor
 from mindspore.ops import prim_attr_register, PrimitiveWithInfer
-
+from mindspore.ops import operations as P
 
 # y = x^2
 class CusSquare(PrimitiveWithInfer):
@@ -26,7 +25,7 @@ class CusSquare(PrimitiveWithInfer):
     def __init__(self):
         """init CusSquare"""
         self.init_prim_io_names(inputs=['x'], outputs=['y'])
-        from square_impl import CusSquare
+        from square_impl import CusSquareImpl
 
     def vm_impl(self, x):
         x = x.asnumpy()
@@ -37,3 +36,10 @@ class CusSquare(PrimitiveWithInfer):
 
     def infer_dtype(self, data_dtype):
         return data_dtype
+    
+    def get_bprop(self):
+        def bprop(data, out, dout):
+            gradient = data * 2
+            dx = gradient * dout
+            return (dx, )
+        return bprop

tests/st/ops/custom_ops_tbe/square_impl.py

@@ -22,12 +22,8 @@ from topi.cce import util
 
 from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
-# shape size limit for aicore is 2**31
-SHAPE_SIZE_LIMIT = 200000000
-
-
 @fusion_manager.register("square")
-def square_compute(input_x, output_y, kernel_name="square"):
+def square_compute(input_x, output_y):
     """
     algorithm: square
     calculating data's square,y= x*x
@@ -50,21 +46,22 @@ def square_compute(input_x, output_y, kernel_name="square"):
     return res
 
 
-cus_conv2D_op_info = TBERegOp("CusSquare") \
+cus_square_op_info = TBERegOp("CusSquare") \
     .fusion_type("OPAQUE") \
     .async_flag(False) \
     .binfile_name("square.so") \
     .compute_cost(10) \
-    .kernel_name("CusSquare") \
+    .kernel_name("CusSquareImpl") \
     .partial_flag(True) \
     .input(0, "x", False, "required", "all") \
     .output(0, "y", False, "required", "all") \
     .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
     .get_op_info()
 
 
-@op_info_register(cus_conv2D_op_info)
-def CusSquare(input_x, output_y, kernel_name="square"):
+@op_info_register(cus_square_op_info)
+def CusSquareImpl(input_x, output_y, kernel_name="CusSquareImpl"):
     """
     algorithm: square
     calculating data's square,y= x*x
@@ -89,7 +86,7 @@ def CusSquare(input_x, output_y, kernel_name="square"):
     data = tvm.placeholder(shape, name="data", dtype=dtype.lower())
 
     with tvm.target.cce():
-        res = square_compute(data, output_y, kernel_name)
+        res = square_compute(data, output_y)
         sch = generic.auto_schedule(res)
 
     config = {"print_ir": False,

tests/st/ops/custom_ops_tbe/test_add3.py

@@ -0,0 +1,44 @@
+# 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 pytest
+import numpy as np
+import mindspore.nn as nn
+import mindspore.context as context
+from mindspore import Tensor
+from mindspore.ops import composite as C
+from cus_add3 import CusAdd3
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+class Net(nn.Cell):
+    """Net definition"""
+
+    def __init__(self):
+        super(Net, self).__init__()
+        self.add3 = CusAdd3(1.0)
+
+    def construct(self, input1, input2):
+        return self.add3(input1, input2)
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.env_onecard
+def test_net():
+    input1 = np.array([1.0, 4.0, 9.0]).astype(np.float32)
+    input2 = np.array([1.0, 2.0, 3.0]).astype(np.float32)
+    add3_net = Net()
+    output = add3_net(Tensor(input1), Tensor(input2))
+    expect = np.array([3.0, 7.0, 13.0]).astype(np.float32)
+    assert (output.asnumpy() == expect).all()

tests/st/ops/custom_ops_tbe/test_square.py

@@ -19,10 +19,9 @@ from cus_square import CusSquare
 import mindspore.context as context
 import mindspore.nn as nn
 from mindspore import Tensor
-
+from mindspore.ops import composite as C
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 
-
 class Net(nn.Cell):
     """Net definition"""
 
@@ -42,7 +41,17 @@ def test_net():
     x = np.array([1.0, 4.0, 9.0]).astype(np.float32)
     square = Net()
     output = square(Tensor(x))
-    print(x)
-    print(output.asnumpy())
     expect = np.array([1.0, 16.0, 81.0]).astype(np.float32)
     assert (output.asnumpy() == expect).all()
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.env_onecard
+def test_grad_net():
+    x = np.array([1.0, 4.0, 9.0]).astype(np.float32)
+    sens = np.array([1.0, 1.0, 1.0]).astype(np.float32)
+    square = Net()
+    dx = C.grad_with_sens(square)(Tensor(x), Tensor(sens))
+    expect = np.array([2.0, 8.0, 18.0]).astype(np.float32)
+    assert (dx.asnumpy() == expect).all()