!5857 [MS][LITE][Develop]support negative,negative grad,log grad
Merge pull request !5857 from chenjianping/lite_dev2pull/5857/MERGE
mindspore-ci-bot
5 years ago
committed by
Gitee
commit
acd896cdea
@ -0,0 +1,37 @@
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/**
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* Copyright 2019-2020 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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#include "src/ops/log_grad.h"
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namespace mindspore {
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namespace lite {
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#ifndef PRIMITIVE_WRITEABLE
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int LogGrad::UnPackToFlatBuilder(const schema::Primitive *primitive, flatbuffers::FlatBufferBuilder *fbb) {
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MS_ASSERT(primitive != nullptr);
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MS_ASSERT(fbb != nullptr);
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auto attr = primitive->value_as_LogGrad();
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if (attr == nullptr) {
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MS_LOG(ERROR) << "value_as_LogGrad return nullptr";
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return RET_ERROR;
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}
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auto val_offset = schema::CreateLogGrad(*fbb);
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auto prim_offset = schema::CreatePrimitive(*fbb, schema::PrimitiveType_LogGrad, val_offset.o);
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fbb->Finish(prim_offset);
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return RET_OK;
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}
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#endif
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} // namespace lite
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} // namespace mindspore
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@ -0,0 +1,42 @@
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/**
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* Copyright 2019-2020 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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#ifndef LITE_MINDSPORE_LITE_C_OPS_LOG_GRAD_H_
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#define LITE_MINDSPORE_LITE_C_OPS_LOG_GRAD_H_
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#include <vector>
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#include <set>
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#include <cmath>
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#include "ir/dtype/type_id.h"
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#include "src/ops/primitive_c.h"
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namespace mindspore {
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namespace lite {
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class LogGrad : public PrimitiveC {
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public:
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#ifdef PRIMITIVE_WRITEABLE
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MS_DECLARE_PARENT(LogGrad, PrimitiveC);
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LogGrad() = default;
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explicit LogGrad(schema::PrimitiveT *primitive) : PrimitiveC(primitive) {}
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#else
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LogGrad() = default;
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int UnPackToFlatBuilder(const schema::Primitive *primitive, flatbuffers::FlatBufferBuilder *fbb) override;
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#endif
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};
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} // namespace lite
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} // namespace mindspore
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#endif // LITE_MINDSPORE_LITE_C_OPS_LOG_GRAD_H_
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@ -0,0 +1,33 @@
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/**
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* Copyright 2019-2020 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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#include "src/ops/neg.h"
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namespace mindspore {
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namespace lite {
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#ifndef PRIMITIVE_WRITEABLE
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int Neg::UnPackToFlatBuilder(const schema::Primitive *primitive, flatbuffers::FlatBufferBuilder *fbb) {
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MS_ASSERT(primitive != nullptr);
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MS_ASSERT(fbb != nullptr);
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auto val_offset = schema::CreateNeg(*fbb);
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auto prim_offset = schema::CreatePrimitive(*fbb, schema::PrimitiveType_Neg, val_offset.o);
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fbb->Finish(prim_offset);
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return RET_OK;
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}
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#endif
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} // namespace lite
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} // namespace mindspore
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@ -0,0 +1,43 @@
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/**
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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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#ifndef LITE_MINDSPORE_LITE_C_OPS_NEG_H_
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#define LITE_MINDSPORE_LITE_C_OPS_NEG_H_
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#include <vector>
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#include <set>
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#include <cmath>
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#include "ir/dtype/type_id.h"
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#include "src/ops/arithmetic_self.h"
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namespace mindspore {
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namespace lite {
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class Neg : public ArithmeticSelf {
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public:
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#ifdef PRIMITIVE_WRITEABLE
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MS_DECLARE_PARENT(Neg, ArithmeticSelf);
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Neg() = default;
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explicit Neg(schema::PrimitiveT *primitive) : ArithmeticSelf(primitive) {}
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#else
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Neg() = default;
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int UnPackToFlatBuilder(const schema::Primitive *primitive, flatbuffers::FlatBufferBuilder *fbb) override;
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#endif
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};
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} // namespace lite
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} // namespace mindspore
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#endif // LITE_MINDSPORE_LITE_C_OPS_NEG_H_
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@ -0,0 +1,33 @@
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/**
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* Copyright 2019-2020 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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#include "src/ops/neg_grad.h"
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namespace mindspore {
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namespace lite {
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#ifndef PRIMITIVE_WRITEABLE
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int NegGrad::UnPackToFlatBuilder(const schema::Primitive *primitive, flatbuffers::FlatBufferBuilder *fbb) {
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MS_ASSERT(primitive != nullptr);
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MS_ASSERT(fbb != nullptr);
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auto val_offset = schema::CreateNegGrad(*fbb);
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auto prim_offset = schema::CreatePrimitive(*fbb, schema::PrimitiveType_NegGrad, val_offset.o);
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fbb->Finish(prim_offset);
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return RET_OK;
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}
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#endif
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} // namespace lite
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} // namespace mindspore
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@ -0,0 +1,43 @@
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/**
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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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#ifndef LITE_MINDSPORE_LITE_C_OPS_NEG_GRAD_H_
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#define LITE_MINDSPORE_LITE_C_OPS_NEG_GRAD_H_
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#include <vector>
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#include <set>
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#include <cmath>
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#include "ir/dtype/type_id.h"
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#include "src/ops/arithmetic_self.h"
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namespace mindspore {
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namespace lite {
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class NegGrad : public ArithmeticSelf {
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public:
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#ifdef PRIMITIVE_WRITEABLE
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MS_DECLARE_PARENT(NegGrad, ArithmeticSelf);
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NegGrad() = default;
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explicit NegGrad(schema::PrimitiveT *primitive) : ArithmeticSelf(primitive) {}
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#else
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NegGrad() = default;
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int UnPackToFlatBuilder(const schema::Primitive *primitive, flatbuffers::FlatBufferBuilder *fbb) override;
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#endif
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};
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} // namespace lite
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} // namespace mindspore
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#endif // LITE_MINDSPORE_LITE_C_OPS_NEG_GRAD_H_
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@ -0,0 +1,107 @@
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/**
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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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#include "src/runtime/kernel/arm/fp32_grad/arithmetic_self_grad.h"
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#include "schema/model_generated.h"
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#include "src/kernel_registry.h"
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#include "include/errorcode.h"
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#include "src/runtime/runtime_api.h"
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#include "nnacl/fp32/arithmetic.h"
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using mindspore::kernel::KERNEL_ARCH::kCPU;
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using mindspore::lite::KernelRegistrar;
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using mindspore::lite::RET_ERROR;
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using mindspore::lite::RET_OK;
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using mindspore::schema::PrimitiveType_LogGrad;
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namespace mindspore::kernel {
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namespace {
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int ArithmeticSelfGradRun(void *cdata, int thread_id) {
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MS_ASSERT(cdata != nullptr);
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auto kernel = reinterpret_cast<ArithmeticSelfGradCPUKernel *>(cdata);
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return kernel->DoArithmeticSelfGrad(thread_id);
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}
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} // namespace
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int ArithmeticSelfGradCPUKernel::Init() {
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auto type = Type();
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switch (type) {
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case PrimitiveType_LogGrad:
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self_grad_operation_ = ElementDiv;
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break;
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default:
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MS_LOG(ERROR) << "Unsupport type: " << type;
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return RET_ERROR;
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}
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return RET_OK;
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}
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int ArithmeticSelfGradCPUKernel::DoArithmeticSelfGrad(int thread_id) {
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auto dy = reinterpret_cast<float *>(in_tensors_[0]->MutableData());
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auto in_x = reinterpret_cast<float *>(in_tensors_[1]->MutableData());
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auto dx = reinterpret_cast<float *>(out_tensors_[0]->MutableData());
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int dy_size = in_tensors_.at(0)->ElementsNum();
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int size = MSMIN(thread_stride_, static_cast<int>(dy_size - thread_id * thread_stride_));
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if (size <= 0) {
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return RET_OK;
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}
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int offset = thread_id * thread_stride_;
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(*self_grad_operation_)(dy + offset, in_x + offset, dx + offset, size);
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return RET_OK;
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}
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int ArithmeticSelfGradCPUKernel::ReSize() { return RET_OK; }
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int ArithmeticSelfGradCPUKernel::Run() {
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int dy_size = in_tensors_.at(0)->ElementsNum();
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op_parameter_->thread_num_ = MSMIN(op_parameter_->thread_num_, static_cast<int>(dy_size));
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thread_stride_ = UP_DIV(dy_size, op_parameter_->thread_num_);
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auto ret = ParallelLaunch(THREAD_POOL_DEFAULT, ArithmeticSelfGradRun, this, op_parameter_->thread_num_);
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if (ret != RET_OK) {
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MS_LOG(ERROR) << "parallel launch fail!ret: " << ret;
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return ret;
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}
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return RET_OK;
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}
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kernel::LiteKernel *CpuArithmeticSelfGradFp32KernelCreator(const std::vector<lite::Tensor *> &inputs,
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const std::vector<lite::Tensor *> &outputs,
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OpParameter *param, const lite::Context *ctx,
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const kernel::KernelKey &desc,
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const mindspore::lite::PrimitiveC *primitive) {
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if (param == nullptr) {
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MS_LOG(ERROR) << "input parameter is nullptr!";
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return nullptr;
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}
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auto *kernel = new (std::nothrow) ArithmeticSelfGradCPUKernel(param, inputs, outputs, ctx, primitive);
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if (kernel == nullptr) {
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MS_LOG(ERROR) << "new ArithmeticSelfGradCPUKernel fail!";
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return nullptr;
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}
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auto ret = kernel->Init();
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if (ret != RET_OK) {
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MS_LOG(ERROR) << "Init kernel failed, name: " << param->name_ << ", type: "
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<< schema::EnumNamePrimitiveType(static_cast<schema::PrimitiveType>(param->type_));
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delete kernel;
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return nullptr;
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}
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return kernel;
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}
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REG_KERNEL(kCPU, kNumberTypeFloat32, PrimitiveType_LogGrad, CpuArithmeticSelfGradFp32KernelCreator)
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} // namespace mindspore::kernel
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@ -0,0 +1,46 @@
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/**
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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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* 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
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* 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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#ifndef MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP32_GRAD_ARITHMETIC_SELF_GRAD_H_
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#define MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP32_GRAD_ARITHMETIC_SELF_GRAD_H_
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#include <vector>
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#include "src/lite_kernel.h"
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#include "schema/model_generated.h"
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#include "ir/anf.h"
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namespace mindspore::kernel {
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class ArithmeticSelfGradCPUKernel : public LiteKernel {
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typedef int (*ArithmeticSelfGradOperation)(float *, float *, float *, int);
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public:
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ArithmeticSelfGradCPUKernel(OpParameter *parameter, const std::vector<lite::Tensor *> &inputs,
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const std::vector<lite::Tensor *> &outputs, const lite::Context *ctx,
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const mindspore::lite::PrimitiveC *primitive)
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: LiteKernel(parameter, inputs, outputs, ctx, primitive) {}
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~ArithmeticSelfGradCPUKernel() override {}
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int Init() override;
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int ReSize() override;
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int Run() override;
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int DoArithmeticSelfGrad(int thread_id);
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private:
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int thread_stride_;
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ArithmeticSelfGradOperation self_grad_operation_;
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};
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} // namespace mindspore::kernel
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#endif // MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP32_GRAD_ARITHMETIC_SELF_GRAD_H_
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@ -0,0 +1,95 @@
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/**
|
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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");
|
||||
* 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.
|
||||
*/
|
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|
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#include "src/runtime/kernel/arm/fp32_grad/neg_grad.h"
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#include "schema/model_generated.h"
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#include "src/kernel_registry.h"
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#include "include/errorcode.h"
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#include "src/runtime/runtime_api.h"
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#include "nnacl/fp32/arithmetic_self.h"
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using mindspore::kernel::KERNEL_ARCH::kCPU;
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using mindspore::lite::KernelRegistrar;
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using mindspore::lite::RET_ERROR;
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using mindspore::lite::RET_OK;
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using mindspore::schema::PrimitiveType_NegGrad;
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namespace mindspore::kernel {
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namespace {
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int NegGradRun(void *cdata, int thread_id) {
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MS_ASSERT(cdata != nullptr);
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auto kernel = reinterpret_cast<NegGradCPUKernel *>(cdata);
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return kernel->DoNegGrad(thread_id);
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}
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} // namespace
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int NegGradCPUKernel::Init() { return RET_OK; }
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int NegGradCPUKernel::DoNegGrad(int thread_id) {
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auto dy = reinterpret_cast<float *>(in_tensors_[0]->MutableData());
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auto dx = reinterpret_cast<float *>(out_tensors_[0]->MutableData());
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int dy_size = in_tensors_.at(0)->ElementsNum();
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int size = MSMIN(thread_stride_, static_cast<int>(dy_size - thread_id * thread_stride_));
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if (size <= 0) {
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return RET_OK;
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}
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int offset = thread_id * thread_stride_;
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ElementNegative(dy + offset, dx + offset, size);
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return RET_OK;
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||||
}
|
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|
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int NegGradCPUKernel::ReSize() { return RET_OK; }
|
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|
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int NegGradCPUKernel::Run() {
|
||||
int dy_size = in_tensors_.at(0)->ElementsNum();
|
||||
op_parameter_->thread_num_ = MSMIN(op_parameter_->thread_num_, static_cast<int>(dy_size));
|
||||
thread_stride_ = UP_DIV(dy_size, op_parameter_->thread_num_);
|
||||
auto ret = ParallelLaunch(THREAD_POOL_DEFAULT, NegGradRun, this, op_parameter_->thread_num_);
|
||||
if (ret != RET_OK) {
|
||||
MS_LOG(ERROR) << "parallel launch fail!ret: " << ret;
|
||||
return ret;
|
||||
}
|
||||
|
||||
return RET_OK;
|
||||
}
|
||||
|
||||
kernel::LiteKernel *CpuNegGradFp32KernelCreator(const std::vector<lite::Tensor *> &inputs,
|
||||
const std::vector<lite::Tensor *> &outputs,
|
||||
OpParameter *param, const lite::Context *ctx,
|
||||
const kernel::KernelKey &desc,
|
||||
const mindspore::lite::PrimitiveC *primitive) {
|
||||
if (param == nullptr) {
|
||||
MS_LOG(ERROR) << "input parameter is nullptr!";
|
||||
return nullptr;
|
||||
}
|
||||
auto *kernel = new (std::nothrow) NegGradCPUKernel(param, inputs, outputs, ctx, primitive);
|
||||
if (kernel == nullptr) {
|
||||
MS_LOG(ERROR) << "new NegGradCPUKernel fail!";
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
auto ret = kernel->Init();
|
||||
if (ret != RET_OK) {
|
||||
MS_LOG(ERROR) << "Init kernel failed, name: " << param->name_ << ", type: "
|
||||
<< schema::EnumNamePrimitiveType(static_cast<schema::PrimitiveType>(param->type_));
|
||||
delete kernel;
|
||||
return nullptr;
|
||||
}
|
||||
return kernel;
|
||||
}
|
||||
|
||||
REG_KERNEL(kCPU, kNumberTypeFloat32, PrimitiveType_NegGrad, CpuNegGradFp32KernelCreator)
|
||||
} // namespace mindspore::kernel
|
||||
@ -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.
|
||||
*/
|
||||
|
||||
#ifndef MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP32_GRAD_NEG_GRAD_H_
|
||||
#define MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP32_GRAD_NEG_GRAD_H_
|
||||
|
||||
#include <vector>
|
||||
#include "src/lite_kernel.h"
|
||||
#include "schema/model_generated.h"
|
||||
#include "ir/anf.h"
|
||||
|
||||
namespace mindspore::kernel {
|
||||
|
||||
class NegGradCPUKernel : public LiteKernel {
|
||||
public:
|
||||
explicit NegGradCPUKernel(OpParameter *parameter, const std::vector<lite::Tensor *> &inputs,
|
||||
const std::vector<lite::Tensor *> &outputs, const lite::Context *ctx,
|
||||
const mindspore::lite::PrimitiveC *primitive)
|
||||
: LiteKernel(parameter, inputs, outputs, ctx, primitive) {}
|
||||
~NegGradCPUKernel() override {}
|
||||
int Init() override;
|
||||
int ReSize() override;
|
||||
int Run() override;
|
||||
int DoNegGrad(int thread_id);
|
||||
|
||||
private:
|
||||
int thread_stride_;
|
||||
};
|
||||
} // namespace mindspore::kernel
|
||||
|
||||
#endif // MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP32_GRAD_NEG_GRAD_H_
|
||||
Loading…
Reference in new issue