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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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from __future__ import absolute_import
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import te.lang.cce
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from te import tvm
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from te.platform.fusion_manager import fusion_manager
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from topi import generic
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from topi.cce import util
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from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
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@fusion_manager.register("add3")
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def add3_compute(input1, input2, const_bias):
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sum2 = te.lang.cce.vadd(input1, input2)
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sum3 = te.lang.cce.vadds(sum2, tvm.const(const_bias, dtype=input1.dtype))
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return sum3
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cus_add3_op_info = TBERegOp("CusAdd3") \
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.fusion_type("OPAQUE") \
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.async_flag(False) \
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.binfile_name("add3.so") \
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.compute_cost(10) \
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.kernel_name("CusAdd3Impl") \
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.partial_flag(True) \
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.attr("const_bias", "required", "float", "all") \
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.input(0, "input1", False, "required", "all") \
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.input(1, "input2", False, "required", "all") \
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.output(0, "sum", False, "required", "all") \
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.dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
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.dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
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.get_op_info()
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@op_info_register(cus_add3_op_info)
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def CusAdd3Impl(input1, inptu2, sum1, const_bias, kernel_name="CusAdd3Impl"):
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shape = input1.get("shape")
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shape = util.shape_refine(shape)
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dtype = input1.get("dtype").lower()
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input1 = tvm.placeholder(shape, name="input1", dtype=dtype.lower())
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input2 = tvm.placeholder(shape, name="input2", dtype=dtype.lower())
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with tvm.target.cce():
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res = add3_compute(input1, input2, const_bias)
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sch = generic.auto_schedule(res)
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config = {"print_ir": False,
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"name": kernel_name,
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"tensor_list": [input1, input2, res]}
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te.lang.cce.cce_build_code(sch, config)
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