f954b2770d
Even though we do not support uint64 op kernels on mobile, it is inevitable to support uint64 tensors in order to enable TF uint64 ops via flex delegate. This CL enables the uint64 tensor type in MLIR converter only. PiperOrigin-RevId: 342939673 Change-Id: I24f422040f82cad7affce4b921361f79e8a51730
125 lines
3.6 KiB
C++
125 lines
3.6 KiB
C++
/* Copyright 2018 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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#include "tensorflow/lite/delegates/flex/util.h"
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namespace tflite {
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namespace flex {
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TfLiteStatus ConvertStatus(TfLiteContext* context,
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const tensorflow::Status& status) {
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if (!status.ok()) {
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context->ReportError(context, "%s", status.error_message().c_str());
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return kTfLiteError;
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}
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return kTfLiteOk;
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}
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TfLiteStatus CopyShapeAndType(TfLiteContext* context,
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const tensorflow::Tensor& src,
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TfLiteTensor* tensor) {
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tensor->type = GetTensorFlowLiteType(static_cast<TF_DataType>(src.dtype()));
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if (tensor->type == kTfLiteNoType) {
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context->ReportError(context,
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"TF Lite does not support TensorFlow data type: %s",
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DataTypeString(src.dtype()).c_str());
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return kTfLiteError;
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}
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int num_dims = src.dims();
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TfLiteIntArray* shape = TfLiteIntArrayCreate(num_dims);
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for (int j = 0; j < num_dims; ++j) {
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// We need to cast from TensorFlow's int64 to TF Lite's int32. Let's
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// make sure there's no overflow.
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if (src.dim_size(j) >= std::numeric_limits<int>::max()) {
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context->ReportError(context,
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"Dimension value in TensorFlow shape is larger than "
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"supported by TF Lite");
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TfLiteIntArrayFree(shape);
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return kTfLiteError;
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}
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shape->data[j] = static_cast<int>(src.dim_size(j));
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}
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return context->ResizeTensor(context, tensor, shape);
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}
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TF_DataType GetTensorFlowDataType(TfLiteType type) {
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switch (type) {
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case kTfLiteNoType:
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return TF_FLOAT;
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case kTfLiteFloat32:
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return TF_FLOAT;
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case kTfLiteFloat16:
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return TF_HALF;
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case kTfLiteFloat64:
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return TF_DOUBLE;
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case kTfLiteInt16:
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return TF_INT16;
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case kTfLiteInt32:
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return TF_INT32;
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case kTfLiteUInt8:
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return TF_UINT8;
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case kTfLiteInt8:
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return TF_INT8;
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case kTfLiteInt64:
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return TF_INT64;
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case kTfLiteUInt64:
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return TF_UINT64;
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case kTfLiteComplex64:
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return TF_COMPLEX64;
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case kTfLiteComplex128:
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return TF_COMPLEX128;
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case kTfLiteString:
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return TF_STRING;
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case kTfLiteBool:
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return TF_BOOL;
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}
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}
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TfLiteType GetTensorFlowLiteType(TF_DataType type) {
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switch (type) {
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case TF_FLOAT:
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return kTfLiteFloat32;
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case TF_HALF:
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return kTfLiteFloat16;
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case TF_DOUBLE:
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return kTfLiteFloat64;
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case TF_INT16:
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return kTfLiteInt16;
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case TF_INT32:
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return kTfLiteInt32;
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case TF_UINT8:
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return kTfLiteUInt8;
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case TF_INT8:
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return kTfLiteInt8;
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case TF_INT64:
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return kTfLiteInt64;
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case TF_UINT64:
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return kTfLiteUInt64;
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case TF_COMPLEX64:
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return kTfLiteComplex64;
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case TF_COMPLEX128:
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return kTfLiteComplex128;
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case TF_STRING:
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return kTfLiteString;
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case TF_BOOL:
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return kTfLiteBool;
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default:
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return kTfLiteNoType;
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}
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}
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} // namespace flex
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} // namespace tflite
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