MLCE-1138 Issue with Delegate supporting FP16 models
* Fixed issue where backends were asked to support FP16 layers that would
be optimized out.
* Fixed issue where backends were asked to support non-constant filter
and bias tensors when those tensors would be replaced by constant
tensors during optimization.
Signed-off-by: Mike Kelly <mike.kelly@arm.com>
Change-Id: Ib54b9cb99d5014e27172841a665daf57d1d5b23d
diff --git a/delegate/classic/src/Convolution.hpp b/delegate/classic/src/Convolution.hpp
index cf0134e..71ecd4c 100644
--- a/delegate/classic/src/Convolution.hpp
+++ b/delegate/classic/src/Convolution.hpp
@@ -56,7 +56,6 @@
{
return kTfLiteError;
}
-
const TfLiteTensor& tfLiteFilterTensor = tfLiteTensors[tfLiteNode->inputs->data[1]];
if (!IsValid(tfLiteContext, tfLiteFilterTensor, operatorCode, nodeIndex))
{
@@ -86,6 +85,7 @@
if(biasEnabled)
{
const TfLiteTensor& tfLiteBiasTensor = tfLiteTensors[tfLiteNode->inputs->data[2]];
+
if (!IsValid(tfLiteContext, tfLiteBiasTensor, operatorCode, nodeIndex))
{
return kTfLiteError;
@@ -115,6 +115,27 @@
armnn::BackendId setBackend;
if (!delegateData.m_Network)
{
+ bool filterIsConst = filterTensorInfo.IsConstant();
+
+ if (!filterIsConst)
+ {
+ filterIsConst = WillInputBeOptimizedToConst(tfLiteContext, tfLiteNode->inputs->data[1]);
+ }
+ armnn::TensorInfo filterTensorInfoCopy(filterTensorInfo);
+ filterTensorInfoCopy.SetConstant(filterIsConst);
+ armnn::Optional<armnn::TensorInfo> optionalBiasInfoCopy(biasTensorInfo);
+
+ if (biasEnabled)
+ {
+ bool biasIsConst = biasTensorInfo.IsConstant();
+
+ if (!biasIsConst)
+ {
+ biasIsConst = WillInputBeOptimizedToConst(tfLiteContext, tfLiteNode->inputs->data[2]);
+ }
+ optionalBiasInfoCopy.value().SetConstant(biasIsConst);
+ }
+
bool isSupported = false;
FORWARD_LAYER_SUPPORT_FUNC("CONV2D",
tfLiteContext,
@@ -125,8 +146,8 @@
inputTensorInfo,
outputTensorInfo,
descriptor,
- filterTensorInfo,
- optionalBiasInfo);
+ filterTensorInfoCopy,
+ optionalBiasInfoCopy);
return isSupported ? kTfLiteOk : kTfLiteError;
}
@@ -480,6 +501,28 @@
armnn::BackendId setBackend;
if (!delegateData.m_Network)
{
+ bool filterIsConst = filterTensorInfo.IsConstant();
+
+ if (!filterIsConst)
+ {
+ filterIsConst = WillInputBeOptimizedToConst(tfLiteContext, tfLiteNode->inputs->data[1]);
+ }
+ armnn::TensorInfo filterTensorInfoCopy(filterTensorInfo);
+ filterTensorInfoCopy.SetConstant(filterIsConst);
+
+ armnn::Optional<armnn::TensorInfo> optionalBiasInfoCopy(biasTensorInfo);
+
+ if (biasEnabled)
+ {
+ bool biasIsConst = biasTensorInfo.IsConstant();
+
+ if (!biasIsConst)
+ {
+ biasIsConst = WillInputBeOptimizedToConst(tfLiteContext, tfLiteNode->inputs->data[2]);
+ }
+ optionalBiasInfoCopy.value().SetConstant(biasIsConst);
+ }
+
bool isSupported = false;
FORWARD_LAYER_SUPPORT_FUNC("DEPTHWISE_CONV2D",
tfLiteContext,
@@ -490,8 +533,8 @@
inputTensorInfo,
outputTensorInfo,
descriptor,
- filterTensorInfo,
- armnn::Optional<armnn::TensorInfo>(biasTensorInfo));
+ filterTensorInfoCopy,
+ optionalBiasInfoCopy);
return isSupported ? kTfLiteOk : kTfLiteError;
}
diff --git a/delegate/classic/src/Quantization.hpp b/delegate/classic/src/Quantization.hpp
index 8291854..7fcb9c7 100644
--- a/delegate/classic/src/Quantization.hpp
+++ b/delegate/classic/src/Quantization.hpp
@@ -23,7 +23,6 @@
{
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))
@@ -34,7 +33,6 @@
tfLiteDequantizeOperatorCode, nodeIndex);
return kTfLiteError;
}
-
const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]];
if (IsDynamicTensor(tfLiteOutputTensor))
{
@@ -54,14 +52,23 @@
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 this is a Dequantize with a Constant input then will be replaced by a Constant layer that contains the
+ // dequantized values during optimization so there's no need to check if it can be supported by the backend
+ if (tflite::IsConstantTensor(&tfLiteInputTensor))
+ {
+ isSupported = true;
+ }
+ else
+ {
+ FORWARD_LAYER_SUPPORT_FUNC("DEQUANTIZE",
+ tfLiteContext,
+ IsDequantizeSupported,
+ delegateData.m_Backends,
+ isSupported,
+ setBackend,
+ inputTensorInfo,
+ outputTensorInfo);
+ }
};
if (!delegateData.m_Network)
diff --git a/delegate/classic/src/SharedFunctions.cpp b/delegate/classic/src/SharedFunctions.cpp
index bcff3a1..53136b5 100644
--- a/delegate/classic/src/SharedFunctions.cpp
+++ b/delegate/classic/src/SharedFunctions.cpp
@@ -110,6 +110,83 @@
return isSupported ? kTfLiteOk : kTfLiteError;
}
+TfLiteNode* GetNodeConnectedToInput(TfLiteContext* tfLiteContext,
+ int32_t& connectedIndex,
+ int32_t inputIdx)
+{
+ TfLiteIntArray* executionPlan = nullptr;
+ if (tfLiteContext->GetExecutionPlan(tfLiteContext, &executionPlan) != kTfLiteOk)
+ {
+ TF_LITE_KERNEL_LOG(tfLiteContext, "TfLiteArmnnDelegate: Unable to get graph execution plan.");
+ return nullptr;
+ }
+
+ for (int i = 0; i < executionPlan->size; ++i)
+ {
+ connectedIndex = executionPlan->data[i];
+
+ // If TfLite nodes can be delegated to ArmNN
+ TfLiteNode* connectedNode = nullptr;
+ TfLiteRegistration* tfLiteRegistration = nullptr;
+ if (tfLiteContext->GetNodeAndRegistration(
+ tfLiteContext, connectedIndex, &connectedNode, &tfLiteRegistration) != kTfLiteOk)
+ {
+ TF_LITE_KERNEL_LOG(tfLiteContext,
+ "TfLiteArmnnDelegate: Unable to get node and registration for node %d.",
+ connectedIndex);
+ continue;
+ }
+ for (int j= 0; j < connectedNode->outputs->size; ++j)
+ {
+ if (connectedNode->outputs->data[j] == inputIdx)
+ {
+ return connectedNode;
+ }
+ }
+ }
+ // No node found so set connectedIndex to -1
+ connectedIndex = -1;
+ return nullptr;
+}
+
+bool WillInputBeOptimizedToConst(TfLiteContext* tfLiteContext, int32_t inputIdx)
+{
+ int32_t connectedIndex;
+ TfLiteNode* connectedNode = GetNodeConnectedToInput(tfLiteContext, connectedIndex, inputIdx);
+
+ if (connectedNode)
+ {
+ TfLiteRegistration* tfLiteRegistration = nullptr;
+
+ if (tfLiteContext->GetNodeAndRegistration(tfLiteContext, connectedIndex, &connectedNode, &tfLiteRegistration)
+ == kTfLiteOk)
+ {
+ switch (tfLiteRegistration->builtin_code)
+ {
+ case kTfLiteBuiltinDequantize:
+ {
+ if (connectedNode->inputs->size >= 1)
+ {
+ const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors;
+ const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[connectedNode->inputs->data[0]];
+
+ // If the input to the Dequantize is a Constant then both that Constant layer and the Dequantize
+ // layer will be replaced by a single Constant layer containing the dequantized values.
+ if (tflite::IsConstantTensor(&tfLiteInputTensor))
+ {
+ return true;
+ }
+ }
+ break;
+ }
+ default:
+ {
+ }
+ }
+ }
+ }
+ return false;
+}
} // namespace armnnDelegate
diff --git a/delegate/classic/src/SharedFunctions.hpp b/delegate/classic/src/SharedFunctions.hpp
index b03a63d..78aa3fe 100644
--- a/delegate/classic/src/SharedFunctions.hpp
+++ b/delegate/classic/src/SharedFunctions.hpp
@@ -21,5 +21,11 @@
const armnn::TensorInfo& outputInfo,
TfLiteFusedActivation activationType);
+TfLiteNode* GetNodeConnectedToInput(TfLiteContext* tfLiteContext,
+ int32_t& connectedIndex,
+ int32_t inputIdx);
+
+bool WillInputBeOptimizedToConst(TfLiteContext* tfLiteContext, int32_t inputIdx);
+
} // namespace armnnDelegate