EVOLUTION-MANAGER

Edit File: assign_op.h

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you may not use this file except in compliance with the License.
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distributed under the License is distributed on an "AS IS" BASIS,
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==============================================================================*/

#ifndef TENSORFLOW_CORE_KERNELS_ASSIGN_OP_H_
#define TENSORFLOW_CORE_KERNELS_ASSIGN_OP_H_

#define EIGEN_USE_THREADS

#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/tensor_types.h"

namespace tensorflow {

// TODO(jeff): Get rid of use_exclusive_lock_ option

// Computes *input[0] = input[1]
class AssignOp : public OpKernel {
 public:
  explicit AssignOp(OpKernelConstruction* context) : OpKernel(context) {
    OP_REQUIRES_OK(context,
                   context->GetAttr("use_locking", &use_exclusive_lock_));
    OP_REQUIRES_OK(context,
                   context->GetAttr("validate_shape", &validate_shape_));
    OP_REQUIRES(context, IsRefType(context->input_type(0)),
                errors::InvalidArgument("lhs input needs to be a ref type"));
    if (!context
             ->GetAttr("_grappler_relax_allocator_constraints",
                       &relax_constraints_)
             .ok()) {
      relax_constraints_ = false;
    }
  }

void Compute(OpKernelContext* context) override {
    const Tensor& rhs = context->input(1);

// We always return the input ref.
    context->forward_ref_input_to_ref_output(0, 0);

// We can't always know how this value will be used downstream, so make
    // conservative assumptions in specifying constraints on the memory
    // allocation attributes, unless the Grappler graph analysis determined that
    // it was safe not to.
    AllocatorAttributes attr;
    if (!relax_constraints_) {
      attr.set_gpu_compatible(true);
      attr.set_nic_compatible(true);
    }

{
      mutex_lock l(*context->input_ref_mutex(0));
      const Tensor& old_lhs = context->mutable_input(0, /* lock_held */ true);
      const bool same_shape = old_lhs.shape().IsSameSize(rhs.shape());
      if (validate_shape_) {
        OP_REQUIRES(context, same_shape,
                    errors::InvalidArgument(
                        "Assign requires shapes of both tensors to match. "
                        "lhs shape= ",
                        old_lhs.shape().DebugString(),
                        " rhs shape= ", rhs.shape().DebugString()));
      }

// In the code below we try to minimize the amount of memory allocation
      // and copying by trying the following two shortcuts:
      // 1. If the lhs is initialized and has the same number of elements as
      //    the rhs we can avoid a memory allocation.
      // 2. If we can reuse the rhs buffer we avoid both a memory allocation
      //    and copying.

// 1. Try to copy into an existing buffer.
      if (old_lhs.IsInitialized() &&
          old_lhs.shape().num_elements() == rhs.shape().num_elements()) {
        // The existing lhs tensor has already been initialized and the right
        // hand side can fit in the underlying buffer.
        Tensor reshaped_old_lhs;
        if (same_shape) {
          reshaped_old_lhs = old_lhs;
        } else {
          CHECK(reshaped_old_lhs.CopyFrom(old_lhs, rhs.shape()));
          context->replace_ref_input(0, reshaped_old_lhs,
                                     /* lock_held */ true);
        }
        if (use_exclusive_lock_) {
          Copy(context, &reshaped_old_lhs, rhs);
          return;
        }
      } else {
        // 2. Try to reuse the rhs.
        std::unique_ptr<Tensor> input_alias = context->forward_input(
            1, OpKernelContext::Params::kNoReservation /*output_index*/,
            rhs.dtype(), rhs.shape(), DEVICE_MEMORY, attr);
        if (input_alias != nullptr) {
          // Update the ref to point to the new buffer.
          context->replace_ref_input(0, *input_alias, /* lock_held */ true);
          return;
        }

// Otherwise, create a new persistent tensor whose shape matches the
        // right hand side, hand off to lhs and copy the rhs into it.
        PersistentTensor copy;
        Tensor* copyTensor = nullptr;
        OP_REQUIRES_OK(
            context, context->allocate_persistent(old_lhs.dtype(), rhs.shape(),
                                                  &copy, &copyTensor, attr));
        // We track memory of variables in variable ops instead of in this
        // assign op.
        context->clear_recorded_memory();
        context->replace_ref_input(0, *copyTensor, /* lock_held */ true);
        if (use_exclusive_lock_) {
          Copy(context, copyTensor, rhs);
          return;
        }
      }
    }

// The tensor has already been initialized and the right hand side
    // matches the left hand side's shape. We have been told to do the
    // copy outside the lock.
    Tensor old_unlocked_lhs = context->mutable_input(0, /* lock_held */ false);
    Copy(context, &old_unlocked_lhs, rhs);
  }

virtual void Copy(OpKernelContext* context, Tensor* lhs,
                    const Tensor& rhs) = 0;

bool use_exclusive_lock_;
  bool validate_shape_;
  bool relax_constraints_;
};

}  // end namespace tensorflow

#endif  // TENSORFLOW_CORE_KERNELS_ASSIGN_OP_H_