experiments/STT-tensorflow
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Minor refactoring the TF_Tensor <-> PyArray conversion functions.

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PiperOrigin-RevId: 163802822
This commit is contained in:
Asim Shankar 2017-08-01 01:18:13 -07:00 committed by TensorFlower Gardener
parent 618f913bbd
commit 6209b4b524
1 changed files with 64 additions and 60 deletions

124
tensorflow/python/client/tf_session_helper.cc
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@ -208,45 +208,52 @@ Status EncodePyBytesArray(PyArrayObject* array, tensorflow::int64 nelems,
return Status::OK();
}
// Determine the pointer and offset of the string at offset 'i' in the string
// tensor 'src', whose total length is 'num_elements'.
static Status TF_StringTensor_GetPtrAndLen(const TF_Tensor* src,
tensorflow::int64 num_elements,
tensorflow::int64 i,
const char** ptr,
tensorflow::uint64* len) {
const char* input = reinterpret_cast<const char*>(TF_TensorData(src));
const size_t src_size = TF_TensorByteSize(src);
const char* data_start = input + sizeof(tensorflow::uint64) * num_elements;
const char* limit = input + src_size;
tensorflow::uint64 offset =
reinterpret_cast<const tensorflow::uint64*>(input)[i];
const char* p =
tensorflow::core::GetVarint64Ptr(data_start + offset, limit, len);
if (static_cast<int64>(offset) >= (limit - data_start) || !p ||
static_cast<int64>(*len) > (limit - p)) {
return errors::InvalidArgument("Malformed TF_STRING tensor; element ", i,
" out of range");
}
*ptr = p;
return Status::OK();
}
Status CopyTF_TensorStringsToPyArray(const TF_Tensor* src, uint64 nelems,
PyArrayObject* dst) {
const void* tensor_data = TF_TensorData(src);
const size_t tensor_size = TF_TensorByteSize(src);
const char* limit = static_cast<const char*>(tensor_data) + tensor_size;
DCHECK(tensor_data != nullptr);
DCHECK_EQ(TF_STRING, TF_TensorType(src));
// Copy the string at offset 'i' in the (linearized) string tensor 'tensor' into
// 'pyarray' at offset pointed by the 'i_ptr' iterator.
static Status CopyStringToPyArrayElement(PyArrayObject* pyarray, void* i_ptr,
TF_Tensor* tensor,
tensorflow::int64 num_elements,
tensorflow::int64 i) {
const char* ptr = nullptr;
tensorflow::uint64 len = 0;
TF_RETURN_IF_ERROR(
TF_StringTensor_GetPtrAndLen(tensor, num_elements, i, &ptr, &len));
auto py_string = tensorflow::make_safe(PyBytes_FromStringAndSize(ptr, len));
int success = PyArray_SETITEM(
pyarray, static_cast<char*>(PyArray_ITER_DATA(i_ptr)), py_string.get());
if (success != 0) {
return errors::Internal("Error setting element ", i);
const uint64* offsets = static_cast<const uint64*>(tensor_data);
const size_t offsets_size = sizeof(uint64) * nelems;
const char* data = static_cast<const char*>(tensor_data) + offsets_size;
const size_t expected_tensor_size =
(limit - static_cast<const char*>(tensor_data));
if (expected_tensor_size - tensor_size) {
return errors::InvalidArgument(
"Invalid/corrupt TF_STRING tensor: expected ", expected_tensor_size,
" bytes of encoded strings for the tensor containing ", nelems,
" strings, but the tensor is encoded in ", tensor_size, " bytes");
}
std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
TF_NewStatus(), TF_DeleteStatus);
auto iter = make_safe(PyArray_IterNew(reinterpret_cast<PyObject*>(dst)));
for (int64 i = 0; i < nelems; ++i) {
const char* start = data + offsets[i];
const char* ptr = nullptr;
size_t len = 0;
TF_StringDecode(start, limit - start, &ptr, &len, status.get());
if (TF_GetCode(status.get()) != TF_OK) {
return errors::InvalidArgument(TF_Message(status.get()));
}
auto py_string = make_safe(PyBytes_FromStringAndSize(ptr, len));
if (py_string.get() == nullptr) {
return errors::Internal(
"failed to create a python byte array when converting element #", i,
" of a TF_STRING tensor to a numpy ndarray");
}
if (PyArray_SETITEM(dst, static_cast<char*>(PyArray_ITER_DATA(iter.get())),
py_string.get()) != 0) {
return errors::Internal("Error settings element #", i,
" in the numpy ndarray");
}
PyArray_ITER_NEXT(iter.get());
}
return Status::OK();
}
@ -304,7 +311,7 @@ Status GetPyArrayDescrForTensor(const TF_Tensor* tensor,
// Converts the given TF_Tensor to a numpy ndarray.
// If the returned status is OK, the caller becomes the owner of *out_array.
Status TFTensorToPyArray(Safe_TF_TensorPtr tensor, PyObject** out_ndarray) {
Status TF_TensorToPyArray(Safe_TF_TensorPtr tensor, PyObject** out_ndarray) {
// A fetched operation will correspond to a null tensor, and a None
// in Python.
if (tensor == nullptr) {
@ -312,15 +319,16 @@ Status TFTensorToPyArray(Safe_TF_TensorPtr tensor, PyObject** out_ndarray) {
*out_ndarray = Py_None;
return Status::OK();
}
if (TF_TensorData(tensor.get()) == nullptr) {
return errors::InvalidArgument(
"TF_Tensor must be in host memory (not device memory) in order to "
"create a numpy ndarray");
}
tensorflow::int64 nelems = -1;
int64 nelems = -1;
gtl::InlinedVector<npy_intp, 4> dims =
GetPyArrayDimensionsForTensor(tensor.get(), &nelems);
// Convert TensorFlow dtype to numpy type descriptor.
PyArray_Descr* descr = nullptr;
TF_RETURN_IF_ERROR(GetPyArrayDescrForTensor(tensor.get(), &descr));
// If the type is neither string nor resource we can reuse the Tensor memory.
TF_Tensor* original = tensor.get();
TF_Tensor* moved = TF_TensorMaybeMove(tensor.release());
@ -335,6 +343,8 @@ Status TFTensorToPyArray(Safe_TF_TensorPtr tensor, PyObject** out_ndarray) {
tensor.reset(original);
// Copy the TF_TensorData into a newly-created ndarray and return it.
PyArray_Descr* descr = nullptr;
TF_RETURN_IF_ERROR(GetPyArrayDescrForTensor(tensor.get(), &descr));
Safe_PyObjectPtr safe_out_array =
tensorflow::make_safe(PyArray_Empty(dims.size(), dims.data(), descr, 0));
if (!safe_out_array) {
@ -343,15 +353,9 @@ Status TFTensorToPyArray(Safe_TF_TensorPtr tensor, PyObject** out_ndarray) {
PyArrayObject* py_array =
reinterpret_cast<PyArrayObject*>(safe_out_array.get());
if (TF_TensorType(tensor.get()) == TF_STRING) {
// Copy element by element.
auto iter = tensorflow::make_safe(PyArray_IterNew(safe_out_array.get()));
for (tensorflow::int64 i = 0; i < nelems; ++i) {
auto s = CopyStringToPyArrayElement(py_array, iter.get(), tensor.get(),
nelems, i);
if (!s.ok()) {
return s;
}
PyArray_ITER_NEXT(iter.get());
Status s = CopyTF_TensorStringsToPyArray(tensor.get(), nelems, py_array);
if (!s.ok()) {
return s;
}
} else if (static_cast<size_t>(PyArray_NBYTES(py_array)) !=
TF_TensorByteSize(tensor.get())) {
@ -370,12 +374,12 @@ Status TFTensorToPyArray(Safe_TF_TensorPtr tensor, PyObject** out_ndarray) {
}
// Converts the given numpy ndarray to a (safe) TF_Tensor. The returned
// TF_Tensor in `out_tensor` will have its own Python reference to `ndarray`s
// TF_Tensor in `out_tensor` may have its own Python reference to `ndarray`s
// data. After `out_tensor` is destroyed, this reference must (eventually) be
// decremented via ClearDecrefCache().
//
// `out_tensor` must be non-null. Caller retains ownership of `ndarray`.
Status PyArrayToTFTensor(PyObject* ndarray, Safe_TF_TensorPtr* out_tensor) {
Status PyArrayToTF_Tensor(PyObject* ndarray, Safe_TF_TensorPtr* out_tensor) {
DCHECK(out_tensor != nullptr);
// Make sure we dereference this array object in case of error, etc.
@ -468,7 +472,7 @@ void TF_Run_wrapper_helper(TF_DeprecatedSession* session, const char* handle,
input_names.push_back(key_string);
inputs_safe.emplace_back(make_safe(static_cast<TF_Tensor*>(nullptr)));
s = PyArrayToTFTensor(value, &inputs_safe.back());
s = PyArrayToTF_Tensor(value, &inputs_safe.back());
if (!s.ok()) {
Set_TF_Status_from_Status(out_status, s);
return;
@ -521,7 +525,7 @@ void TF_Run_wrapper_helper(TF_DeprecatedSession* session, const char* handle,
std::vector<Safe_PyObjectPtr> py_outputs_safe;
for (size_t i = 0; i < output_names.size(); ++i) {
PyObject* py_array;
s = TFTensorToPyArray(std::move(tf_outputs_safe[i]), &py_array);
s = TF_TensorToPyArray(std::move(tf_outputs_safe[i]), &py_array);
if (!s.ok()) {
Set_TF_Status_from_Status(out_status, s);
return;
@ -601,7 +605,7 @@ void TF_SessionRun_wrapper_helper(TF_Session* session, const char* handle,
// cleaned up properly.
//
// Memory management:
// PyArrayToTFTensor() creates a new ndarray PyObject from the input
// PyArrayToTF_Tensor() creates a new ndarray PyObject from the input
// ndarray. We manage the new ndarray's lifetime in order to keep the
// underlying data buffer alive (the new ndarray also guarantees a contiguous
// data buffer). The new ndarray's data buffer is used to create the
@ -616,7 +620,7 @@ void TF_SessionRun_wrapper_helper(TF_Session* session, const char* handle,
std::vector<Safe_TF_TensorPtr> input_vals_safe;
for (PyObject* ndarray : input_ndarrays) {
input_vals_safe.emplace_back(make_safe(static_cast<TF_Tensor*>(nullptr)));
s = PyArrayToTFTensor(ndarray, &input_vals_safe.back());
s = PyArrayToTF_Tensor(ndarray, &input_vals_safe.back());
if (!s.ok()) {
Set_TF_Status_from_Status(out_status, s);
return;
@ -654,7 +658,7 @@ void TF_SessionRun_wrapper_helper(TF_Session* session, const char* handle,
std::vector<Safe_PyObjectPtr> py_outputs_safe;
for (size_t i = 0; i < outputs.size(); ++i) {
PyObject* py_array;
s = TFTensorToPyArray(std::move(output_vals_safe[i]), &py_array);
s = TF_TensorToPyArray(std::move(output_vals_safe[i]), &py_array);
if (!s.ok()) {
Set_TF_Status_from_Status(out_status, s);
return;