add cross-entropy-op (#2965)
* add cross-entropy-op * add infershape and compute * implement Infershape and compute of onehotcrossentropy opcblas_new
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c90b94e85c
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2685765905
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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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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#include "paddle/operators/cross_entropy_op.h"
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#include "paddle/framework/op_registry.h"
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#include "paddle/framework/tensor.h"
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namespace paddle {
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namespace operators {
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class OnehotCrossEntropyOp : public framework::OperatorWithKernel {
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protected:
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void InferShape(
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const std::vector<const framework::Tensor *> &inputs,
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const std::vector<framework::Tensor *> &outputs) const override {
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PADDLE_ENFORCE(inputs.size() == 2,
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"Input size of OnehotCrossEntropyOp must be two");
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PADDLE_ENFORCE(outputs.size() == 1,
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"Output size of OnehotCrossEntropyOp must be one");
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PADDLE_ENFORCE(inputs[0] != nullptr && inputs[1] != nullptr,
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"Inputs of OnehotCrossEntropyOp must all be set");
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PADDLE_ENFORCE(outputs[0] != nullptr,
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"Outputs of OnehotCrossEntropyOp must all be set");
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PADDLE_ENFORCE(inputs[0]->dims().size() == 2, "X's dimension must be 2.");
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PADDLE_ENFORCE(outputs[0]->dims().size() == 1,
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"label's dimension must be 1.");
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outputs[0]->set_dims(framework::make_ddim({inputs[0]->dims()[0]}));
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}
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};
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class OnehotCrossEntropyOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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OnehotCrossEntropyOpMaker(framework::OpProto *proto,
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framework::OpAttrChecker *op_checker)
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: framework::OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("X", "The first input of OnehotCrossEntropyOp");
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AddInput("label", "The second input of OnehotCrossEntropyOp");
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AddOutput("Y", "The output of OnehotCrossEntropyOp");
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AddComment(R"DOC(
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OnehotCrossEntropy Operator.
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Y[i] = -log(X[i][j])
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)DOC");
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}
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};
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} // namespace operators
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} // namespace paddle
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REGISTER_OP(onehot_cross_entropy,
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paddle::operators::OnehotCrossEntropyOp,
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paddle::operators::OnehotCrossEntropyOpMaker);
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REGISTER_OP_CPU_KERNEL(
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onehot_cross_entropy,
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paddle::operators::OnehotCrossEntropyOpKernel<::paddle::platform::CPUPlace,
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float>);
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#include "paddle/operators/cross_entropy_op.h"
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#include "paddle/framework/op_registry.h"
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REGISTER_OP_GPU_KERNEL(onehot_cross_entropy,
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paddle::operators::OnehotCrossEntropyOpKernel<
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::paddle::platform::GPUPlace, float>);
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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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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#pragma once
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#include "glog/logging.h"
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#include "paddle/framework/operator.h"
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namespace paddle {
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namespace operators {
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template <typename Place, typename T>
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class OnehotCrossEntropyOpKernel : public framework::OpKernel {
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public:
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constexpr T LOG_THRESHOLD() const { return static_cast<T>(1e-20); }
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void Compute(const framework::KernelContext& context) const override {
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auto X = context.Input(0)->Get<framework::Tensor>();
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const T* X_data = X.data<T>();
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const int* label_data =
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context.Input(1)->Get<framework::Tensor>().data<int>();
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auto* Y = context.Output(0)->GetMutable<framework::Tensor>();
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Y->mutable_data<T>(context.GetPlace());
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T* Y_data = Y->data<T>();
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int batch_size = X.dims()[0];
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int class_num = X.dims()[1];
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// Y[i] = -log(X[i][j])
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for (int i = 0; i < batch_size; ++i) {
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Y_data[i] = -std::log(
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std::max(X_data[i * class_num + label_data[i]], LOG_THRESHOLD()));
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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@ -1,2 +1,2 @@
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cc_library(paddle_pybind SHARED SRCS pybind.cc DEPS pybind python
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add_op fc_op sgd_op)
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add_op fc_op sgd_op cross_entropy_op)
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add_python_test(test_framework test_protobuf.py test_scope.py
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test_default_scope_funcs.py test_op_creation_methods.py
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test_tensor.py test_fc_op.py test_add_two_op.py test_sgd_op.py)
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test_tensor.py test_fc_op.py test_add_two_op.py test_sgd_op.py test_cross_entropy_op.py)
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import unittest
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import numpy
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from op_test_util import OpTestMeta
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class TestSGD(unittest.TestCase):
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__metaclass__ = OpTestMeta
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def setUp(self):
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self.type = "onehot_cross_entropy"
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batch_size = 100
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class_num = 10
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self.X = numpy.random.random((batch_size, class_num)).astype("float32")
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self.label = 5 * numpy.ones(batch_size).astype("int32")
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Y = []
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for i in range(0, batch_size):
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Y.append(-numpy.log(self.X[i][self.label[i]]))
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self.Y = numpy.array(Y).astype("float32")
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if __name__ == "__main__":
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unittest.main()
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