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/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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 TENSORFLOW_C_EAGER_C_API_H_
#define TENSORFLOW_C_EAGER_C_API_H_
// C API extensions to experiment with eager execution of kernels.
#include "tensorflow/c/c_api.h"
#ifdef __cplusplus
extern "C" {
#endif
// "Context" under which operations/functions are executed. It encapsulates
// things like the available devices, resource manager etc.
//
// TODO(ashankar): Merge with TF_Session?
typedef struct TFE_Context TFE_Context;
extern TFE_Context* TFE_NewContext(const TF_SessionOptions* opts,
TF_Status* status);
extern void TFE_DeleteContext(TFE_Context* ctx, TF_Status* status);
extern TF_DeviceList* TFE_ContextListDevices(TFE_Context* ctx,
TF_Status* status);
// A handle to a tensor on a device.
//
// Like a TF_Tensor, a TFE_TensorHandle refers to a tensor with a value, shape,
// type etc. Unlike a TF_Tensor, a TFE_TensorHandle may refer to such tensors
// placed in memory of different devices or remote address spaces.
typedef struct TFE_TensorHandle TFE_TensorHandle;
extern TFE_TensorHandle* TFE_NewTensorHandle(TF_Tensor* t, TF_Status* status);
extern void TFE_DeleteTensorHandle(TFE_TensorHandle* h);
extern TF_DataType TFE_TensorHandleDataType(TFE_TensorHandle* h);
extern int TFE_TensorHandleNumDims(TFE_TensorHandle* h);
extern int64_t TFE_TensorHandleDim(TFE_TensorHandle* h, int dim_index);
extern const char* TFE_TensorHandleDeviceName(TFE_TensorHandle* h);
extern TF_Tensor* TFE_TensorHandleResolve(TFE_TensorHandle* h,
TF_Status* status);
// Create a new TFE_TensorHandle with the same contents as 'h' but placed
// in the memory of the device name 'device_name'.
// If source and destination are the same device, then this creates a new handle
// that shares the underlying buffer. Otherwise, it currently requires at least
// one of the source or destination devices to be CPU (i.e., for the source or
// destination tensor to be placed in host memory).
extern TFE_TensorHandle* TFE_TensorHandleCopyToDevice(TFE_TensorHandle* h,
TFE_Context* ctx,
const char* device_name,
TF_Status* status);
// Description of the TensorFlow op to execute.
//
// Assumes that the provided 'ctx' outlives the returned TFE_Op, i.e.,
// TFE_DeleteOp() is called before TFE_DeleteContext().
//
// Very similar to TF_OperationDescription with some differences:
// (1) TF_Output or TFE_TensorHandle* as arguments to TF_AddInput,
// TF_AddInputList
// (2) TF_ColocateWith, TF_AddControlInput etc. do not make sense.
// (3) Implementation detail: Avoid use of NodeBuilder/NodeDefBuilder since
// the additional sanity checks there seem unnecessary;
typedef struct TFE_Op TFE_Op;
extern TFE_Op* TFE_NewOp(TFE_Context* ctx, const char* op_or_function_name,
TF_Status* status);
extern void TFE_DeleteOp(TFE_Op* op);
// TODO(ashankar): TFE_OpSetDevice and TFE_Execute should not have a TFE_Context
// parameter. Instead, the TFE_Context should be captured when creating the
// TFE_Op.
extern void TFE_OpSetDevice(TFE_Op* op, TFE_Context* ctx,
const char* device_name, TF_Status* status);
extern void TFE_OpAddInput(TFE_Op* op, TFE_TensorHandle* h, TF_Status* status);
extern TF_AttrType TFE_OpGetAttrType(TFE_Op* op, const char* attr_name,
unsigned char* is_list, TF_Status* status);
extern void TFE_OpSetAttrString(TFE_Op* op, const char* attr_name,
const char* value);
extern void TFE_OpSetAttrInt(TFE_Op* op, const char* attr_name, int64_t value);
extern void TFE_OpSetAttrFloat(TFE_Op* op, const char* attr_name, float value);
extern void TFE_OpSetAttrBool(TFE_Op* op, const char* attr_name,
unsigned char value);
extern void TFE_OpSetAttrType(TFE_Op* op, const char* attr_name,
TF_DataType value);
// If the number of dimensions is unknown, `num_dims` must be set to
// -1 and `dims` can be null. If a dimension is unknown, the
// corresponding entry in the `dims` array must be -1.
extern void TFE_OpSetAttrShape(TFE_Op* op, const char* attr_name,
const int64_t* dims, const int num_dims,
TF_Status* out_status);
extern void TFE_OpSetAttrStringList(TFE_Op* op, const char* attr_name,
const char** value, int num_values);
extern void TFE_OpSetAttrIntList(TFE_Op* op, const char* attr_name,
const int64_t* values, int num_values);
extern void TFE_OpSetAttrFloatList(TFE_Op* op, const char* attr_name,
const float* values, int num_values);
extern void TFE_OpSetAttrBoolList(TFE_Op* op, const char* attr_name,
const unsigned char* values, int num_values);
extern void TFE_OpSetAttrTypeList(TFE_Op* op, const char* attr_name,
const TF_DataType* values, int num_values);
extern void TFE_OpSetAttrShapeList(TFE_Op* op, const char* attr_name,
const int64_t** dims, const int* num_dims,
int num_values, TF_Status* out_status);
// Execute the operation defined by 'op' and return handles to computed
// tensors in 'retvals'.
//
// 'retvals' must point to a pre-allocated array of TFE_TensorHandle*
// and '*num_retvals' should be set to the size of this array.
//
// On return, 'num_retvals' will be set to the actual number of outputs
// returned by the operation.
extern void TFE_Execute(TFE_Op* op, TFE_TensorHandle** retvals,
int* num_retvals, TF_Status* status);
// Add a function (serialized FunctionDef protocol buffer) to ctx so
// that it can be invoked using TFE_Execute.
extern void TFE_ContextAddFunctionDef(TFE_Context* ctx,
const char* serialized_function_def,
size_t size, TF_Status* status);
#ifdef __cplusplus
} /* end extern "C" */
#endif
#ifdef __cplusplus
// A workaround to ease conversion to and from numpy objects and
// TFE_TensorHandle's.
//
// TODO(ashankar): Figure out an alternative scheme that precludes the need for
// these API-boundary breaking methods.
namespace tensorflow {
class Tensor;
} // namespace tensorflow
const tensorflow::Tensor* TFE_TensorHandleUnderlyingTensorInHostMemory(
TFE_TensorHandle* h, TF_Status* status);
TFE_TensorHandle* TFE_NewTensorHandle(const tensorflow::Tensor& t);
#endif
#endif // TENSORFLOW_C_EAGER_C_API_H_
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