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Diffstat (limited to 'delegate/classic/src/Quantization.hpp')
| -rw-r--r-- | delegate/classic/src/Quantization.hpp | 171 |
1 files changed, 171 insertions, 0 deletions
diff --git a/delegate/classic/src/Quantization.hpp b/delegate/classic/src/Quantization.hpp new file mode 100644 index 0000000000..f1192960e4 --- /dev/null +++ b/delegate/classic/src/Quantization.hpp @@ -0,0 +1,171 @@ +// +// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#pragma once + +#include <armnn/utility/IgnoreUnused.hpp> + +#include <tensorflow/lite/builtin_ops.h> +#include <tensorflow/lite/c/builtin_op_data.h> +#include <tensorflow/lite/c/common.h> +#include <tensorflow/lite/minimal_logging.h> + +namespace armnnDelegate +{ + +TfLiteStatus VisitDequantizeOperator(DelegateData& delegateData, + TfLiteContext* tfLiteContext, + TfLiteNode* tfLiteNode, + int nodeIndex, + int32_t tfLiteDequantizeOperatorCode) +{ + TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); + TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); + + const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; + const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; + if (IsDynamicTensor(tfLiteInputTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", + tfLiteDequantizeOperatorCode, nodeIndex); + return kTfLiteError; + } + + const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; + if (IsDynamicTensor(tfLiteOutputTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ", + tfLiteDequantizeOperatorCode, nodeIndex); + + return kTfLiteError; + } + const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); + armnn::TensorInfo outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); + + UpdateConstantTensorOutputs(inputTensorInfo, outputTensorInfo); + + bool isSupported = false; + armnn::BackendId setBackend; + auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) + { + FORWARD_LAYER_SUPPORT_FUNC("DEQUANTIZE", + tfLiteContext, + IsDequantizeSupported, + delegateData.m_Backends, + isSupported, + setBackend, + inputTensorInfo, + outputTensorInfo); + }; + + if (!delegateData.m_Network) + { + validateFunc(outputTensorInfo, isSupported); + return isSupported ? kTfLiteOk : kTfLiteError; + } + + armnn::IConnectableLayer* dequantizeLayer = delegateData.m_Network->AddDequantizeLayer(); + dequantizeLayer->SetBackendId(setBackend); + ARMNN_ASSERT(dequantizeLayer != nullptr); + + armnn::IOutputSlot& outputSlot = dequantizeLayer->GetOutputSlot(0); + outputSlot.SetTensorInfo(outputTensorInfo); + + auto inputsTensorsProcess = ProcessInputs(dequantizeLayer, + delegateData, + tfLiteContext, + tfLiteNode); + if (inputsTensorsProcess == kTfLiteError) + { + return inputsTensorsProcess; + } + + return Connect(dequantizeLayer, tfLiteNode, delegateData); +} + +TfLiteStatus VisitQuantizeOperator(DelegateData& delegateData, + TfLiteContext* tfLiteContext, + TfLiteNode* tfLiteNode, + int nodeIndex, + int32_t tfLiteQuantizeOperatorCode) +{ + TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); + TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); + + const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; + const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; + if (IsDynamicTensor(tfLiteInputTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", + tfLiteQuantizeOperatorCode, nodeIndex); + return kTfLiteError; + } + + const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; + if (IsDynamicTensor(tfLiteOutputTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ", + tfLiteQuantizeOperatorCode, nodeIndex); + return kTfLiteError; + } + + // Only affine per-layer quantization is supported. + if (!IsAffineQuantization(tfLiteOutputTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Only affine per-layer quantization is supported in operator #%d node #%d: ", + tfLiteQuantizeOperatorCode, nodeIndex); + return kTfLiteError; + } + + const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); + const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); + + bool isSupported = false; + armnn::BackendId setBackend; + auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) + { + FORWARD_LAYER_SUPPORT_FUNC("QUANTIZE", + tfLiteContext, + IsQuantizeSupported, + delegateData.m_Backends, + isSupported, + setBackend, + inputTensorInfo, + outputTensorInfo); + }; + + if (!delegateData.m_Network) + { + validateFunc(outputTensorInfo, isSupported); + return isSupported ? kTfLiteOk : kTfLiteError; + } + + armnn::IConnectableLayer* quantizeLayer = delegateData.m_Network->AddQuantizeLayer(); + quantizeLayer->SetBackendId(setBackend); + ARMNN_ASSERT(quantizeLayer != nullptr); + + armnn::IOutputSlot& outputSlot = quantizeLayer->GetOutputSlot(0); + outputSlot.SetTensorInfo(outputTensorInfo); + + // try to connect the Constant Inputs if there are any + if(ProcessInputs(quantizeLayer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk ) + { + return kTfLiteError; + } + + return Connect(quantizeLayer, tfLiteNode, delegateData); +} + +} // namespace armnnDelegate |