109 lines
4.6 KiB
C++
109 lines
4.6 KiB
C++
/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
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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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==============================================================================*/
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#ifndef TENSORFLOW_C_EAGER_MNIST_GRADIENTS_TESTUTIL_H_
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#define TENSORFLOW_C_EAGER_MNIST_GRADIENTS_TESTUTIL_H_
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#include <memory>
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#include "absl/types/span.h"
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#include "tensorflow/c/eager/abstract_tensor_handle.h"
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#include "tensorflow/c/eager/c_api_experimental.h"
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#include "tensorflow/c/eager/c_api_unified_experimental.h"
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#include "tensorflow/c/eager/c_api_unified_experimental_internal.h"
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#include "tensorflow/c/eager/gradients.h"
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#include "tensorflow/c/eager/gradients_internal.h"
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#include "tensorflow/c/experimental/ops/array_ops.h"
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#include "tensorflow/c/experimental/ops/math_ops.h"
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#include "tensorflow/c/experimental/ops/nn_ops.h"
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#include "tensorflow/core/lib/llvm_rtti/llvm_rtti.h"
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#include "tensorflow/core/platform/status.h"
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namespace tensorflow {
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namespace gradients {
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namespace internal {
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// Computes
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// y = inputs[0] + inputs[1]
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// return grad(y, {inputs[0], inputs[1]})
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Status AddGradModel(AbstractContext* ctx,
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absl::Span<AbstractTensorHandle* const> inputs,
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absl::Span<AbstractTensorHandle*> outputs,
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const GradientRegistry& registry);
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// Computes
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// y = inputs[0] * inputs[1]
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// return grad(y, {inputs[0], inputs[1]})
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Status MatMulGradModel(AbstractContext* ctx,
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absl::Span<AbstractTensorHandle* const> inputs,
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absl::Span<AbstractTensorHandle*> outputs,
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const GradientRegistry& registry);
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// Computes 2-layer Neural Network with Softmax Loss.
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Status MNISTForwardModel(AbstractContext* ctx,
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absl::Span<AbstractTensorHandle* const> inputs,
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absl::Span<AbstractTensorHandle*> outputs,
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const GradientRegistry& registry);
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// Computes MatMul with first matrix tranposed.
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Status MatMulTransposeModel(AbstractContext* ctx,
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absl::Span<AbstractTensorHandle* const> inputs,
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absl::Span<AbstractTensorHandle*> outputs,
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const GradientRegistry& registry);
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// Test Model to verify ReluGrad functionality
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Status ReluGradModel(AbstractContext* ctx,
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absl::Span<AbstractTensorHandle* const> inputs,
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absl::Span<AbstractTensorHandle*> outputs,
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const GradientRegistry& registry);
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// Test Model to verify SoftmaxGrad functionality
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Status SoftmaxLossGradModel(AbstractContext* ctx,
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absl::Span<AbstractTensorHandle* const> inputs,
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absl::Span<AbstractTensorHandle*> outputs,
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const GradientRegistry& registry);
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// Test Model to verify Multi-grad functionality for MNIST
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Status MNISTGradModel(AbstractContext* ctx,
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absl::Span<AbstractTensorHandle* const> inputs,
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absl::Span<AbstractTensorHandle*> outputs,
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const GradientRegistry& registry);
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// Test Model to verify scalar-tensor multiplication Op
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Status ScalarMulModel(AbstractContext* ctx,
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absl::Span<AbstractTensorHandle* const> inputs,
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absl::Span<AbstractTensorHandle*> outputs,
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const GradientRegistry& registry);
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Status MatMulModel(AbstractContext* ctx,
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absl::Span<AbstractTensorHandle* const> inputs,
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absl::Span<AbstractTensorHandle*> outputs,
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const GradientRegistry& registry);
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Status MulModel(AbstractContext* ctx,
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absl::Span<AbstractTensorHandle* const> inputs,
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absl::Span<AbstractTensorHandle*> outputs,
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const GradientRegistry& registry);
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Status SoftmaxModel(AbstractContext* ctx,
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absl::Span<AbstractTensorHandle* const> inputs,
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absl::Span<AbstractTensorHandle*> outputs,
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const GradientRegistry& registry);
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} // namespace internal
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} // namespace gradients
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} // namespace tensorflow
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#endif // TENSORFLOW_C_EAGER_MNIST_GRADIENTS_TESTUTIL_H_
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