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mymatmul_grad.cc
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mymatmul_grad.cc
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/// \file inner_product_grad.cca
/// \author David Stutz
/// \brief Implementation of the gradient of a inner product operation, see
/// inner_product.cc.
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/shape_inference.h"
using namespace tensorflow;
// the gradients are simply passed as additional arguments as
// they are available in the Python function for registering the gradient operation.
REGISTER_OP("MyMatmulGrad")
.Input("grad: double")
.Input("weights: double")
.Input("input: double")
.Output("grad_weights: double")
.Output("grad_input: double");
/// \brief Implementation of an inner product gradient operation.
/// Note that this operation is used in Python to register the gradient as
/// this is not possible in C*+ right now.
/// \param context
/// \author David Stutz
class MyMatmulGradOp : public OpKernel {
public:
/// \brief Constructor.
/// \param context
explicit MyMatmulGradOp(OpKernelConstruction* context) : OpKernel(context) {
}
/// \brief Compute the inner product gradients.
/// \param context
void Compute(OpKernelContext* context) override {
// output and grad is provided as input
DCHECK_EQ(3, context->num_inputs());
// get the gradient tensor
const Tensor& grad = context->input(0);
// get the original input tensor
const Tensor& input = context->input(2);
// get the weight tensor
const Tensor& weights = context->input(1);
// create input shape (inferred from the additional attribute `n`)
TensorShape input_shape = input.shape();
TensorShape weights_shape = weights.shape();
DCHECK_EQ(input_shape.dim_size(0), weights_shape.dim_size(1));
DCHECK_EQ(weights_shape.dim_size(0), grad.shape().dim_size(0));
// create output tensors
Tensor* grad_input = NULL;
Tensor* grad_weights = NULL;
OP_REQUIRES_OK(context, context->allocate_output(1, input_shape, &grad_input));
OP_REQUIRES_OK(context, context->allocate_output(0, weights_shape, &grad_weights));
// get the Eigen tensors for data access
auto grad_tensor = grad.matrix<double>();
auto weights_tensor = weights.matrix<double>();
auto input_tensor = input.matrix<double>();
auto grad_input_tensor = grad_input->matrix<double>();
auto grad_weights_tensor = grad_weights->matrix<double>();
// std::cout << "\ninput_tensor1: " << input_tensor/*.format(fmt)*/ << std::endl;
// doign it manually for ismplicity
for (int i = 0; i < input_shape.dim_size(0); i++) {
grad_input_tensor(i, 0) = 0;
for (int j = 0; j < grad.shape().dim_size(0); j++) {
grad_input_tensor(i, 0) += grad_tensor(j, 0)*weights_tensor(j, i);
// std::cout << "i: " << i << ",j: " << j << "\ninput_tensor1: " << input_tensor/*.format(fmt)*/ << std::endl;
}
}
// std::cout << "\ninput_tensor2: " << input_tensor/*.format(fmt)*/ << std::endl;
// print the grad_tensor and the input_tensor
// const Eigen::IOFormat fmt(2, Eigen::DontAlignCols, "\t", " ", "", "");
for (int i = 0; i < weights_shape.dim_size(0); i++) {
for (int j = 0; j < weights_shape.dim_size(1); j++) {
grad_weights_tensor(i, j) = grad_tensor(i, 0)*input_tensor(j, 0);;
}
}
}
};
REGISTER_KERNEL_BUILDER(Name("MyMatmulGrad").Device(DEVICE_CPU), MyMatmulGradOp);