From 5644fdfc7c060a069753a15969d9316f5a90cf54 Mon Sep 17 00:00:00 2001
From: Hashim Sharif <hsharif3@miranda.cs.illinois.edu>
Date: Thu, 24 Jun 2021 21:47:17 -0500
Subject: [PATCH] Correctly handling readInputBatch -- arg ordering changed
---
.../Transforms/HPVM2NVDLA/HPVM2NVDLAPass.cpp | 110 +++++++++++++-----
1 file changed, 81 insertions(+), 29 deletions(-)
diff --git a/hpvm/lib/Transforms/HPVM2NVDLA/HPVM2NVDLAPass.cpp b/hpvm/lib/Transforms/HPVM2NVDLA/HPVM2NVDLAPass.cpp
index 87f706a77e..fced0b3ccd 100644
--- a/hpvm/lib/Transforms/HPVM2NVDLA/HPVM2NVDLAPass.cpp
+++ b/hpvm/lib/Transforms/HPVM2NVDLA/HPVM2NVDLAPass.cpp
@@ -21,6 +21,7 @@
#include <vector>
#include <map>
#include <set>
+#include <string.h>
#include "dlaerror.h"
#include "dlatypes.h"
@@ -347,6 +348,7 @@ Weights CGT_NVDLA::readTrainedWeights(User *TensorPtr,
return Weight;
}
+
// For a tensor to be a input weight tensor, it has to come from the root node
User *CGT_NVDLA::getBindingTensor(DFLeafNode* N, unsigned index) {
// HPVM internal API needs fixing. Remove this lambda function when bug is fixed.
@@ -508,9 +510,16 @@ User *CGT_NVDLA::getBindingTensor(DFLeafNode* N, unsigned index) {
}
}
}
+
+ errs() << *ArgObj << " : "<< *ArgObj->getType() << "\n";
auto *ArgObjPtrType = dyn_cast<PointerType>(ArgObj->getType());
+ // FIXIT: LLVM-9 handling of structs/pointers may be different.
+ // This assertion is firing
+
auto *ArgObjType = dyn_cast<StructType>(ArgObjPtrType->getElementType());
- assert(ArgObjType && "Arguments to launch is a structure.");
+ // HASH-NOTE: removed assertion for the time being - i8* root struct
+ // HASH-NOTE: CANNOT remove this assertion. The DL of Struct is to be used later
+ //assert(ArgObjType && "Arguments to launch is a structure.");
DEBUG(errs() << "ARG OBJ: ");
DEBUG(ArgObj->print(errs()));
DEBUG(errs() << "\n");
@@ -608,27 +617,61 @@ void CGT_NVDLA::getaddOpSucceedsNode(DFNode *N, SmallVector<DFLeafNode *, 4> &Ad
}
ITensor *CGT_NVDLA::getNVDLAInputTensor(DFLeafNode* N, const User *InputBindingTensor) {
- if(InputBindingTensor) {
- auto *BatchesConst = dyn_cast<ConstantInt>(InputBindingTensor->getOperand(2));
- auto *ChannelsConst = dyn_cast<ConstantInt>(InputBindingTensor->getOperand(3));
- auto *HeightConst = dyn_cast<ConstantInt>(InputBindingTensor->getOperand(4));
- auto *WidthConst = dyn_cast<ConstantInt>(InputBindingTensor->getOperand(5));
- assert(HeightConst && WidthConst && ChannelsConst && BatchesConst
+
+ if(InputBindingTensor) {
+
+ ConstantInt* BatchesConst;
+ ConstantInt* ChannelsConst;
+ ConstantInt* WidthConst;
+ ConstantInt* HeightConst;
+
+ const CallInst* readCall = dyn_cast<CallInst>(InputBindingTensor);
+ const char* functionName = readCall->getCalledFunction()->getName().data();
+
+ if(strstr(functionName, "readInputBatch") != NULL){
+ errs()<< "inputFunction = " << functionName<<" \n";
+
+ BatchesConst = ConstantInt::get(Type::getInt32Ty(M.getContext()), 1);
+ ChannelsConst = dyn_cast<ConstantInt>(InputBindingTensor->getOperand(4));
+ HeightConst = dyn_cast<ConstantInt>(InputBindingTensor->getOperand(5));
+ WidthConst = dyn_cast<ConstantInt>(InputBindingTensor->getOperand(6));
+
+ errs()<< *InputBindingTensor <<"\n";
+ errs()<< *BatchesConst << "\n";
+ errs()<< *ChannelsConst << "\n";
+
+ }
+ else{
+
+ errs()<< " weightsFunction = " << functionName<<" \n";
+
+ BatchesConst = dyn_cast<ConstantInt>(InputBindingTensor->getOperand(2));
+ ChannelsConst = dyn_cast<ConstantInt>(InputBindingTensor->getOperand(3));
+ HeightConst = dyn_cast<ConstantInt>(InputBindingTensor->getOperand(4));
+ WidthConst = dyn_cast<ConstantInt>(InputBindingTensor->getOperand(5));
+ }
+
+
+ assert(HeightConst && WidthConst && ChannelsConst && BatchesConst
&& "Number of input dimensions must be constants.");
- // Input dimensions
- int InputW = WidthConst->getZExtValue();
- int InputH = HeightConst->getZExtValue();
- int InputC = ChannelsConst->getZExtValue();
- int InputN = BatchesConst->getZExtValue();
-
- // Create a new input tensor
- Dims4 dims(InputN, InputC, InputH, InputW);
- return Network->addInput("", dims);
- }
- return getIntermediateInputTensor(N);
+
+ // Input dimensions
+ int InputW = WidthConst->getZExtValue();
+ int InputH = HeightConst->getZExtValue();
+ int InputC = ChannelsConst->getZExtValue();
+ int InputN = BatchesConst->getZExtValue();
+
+ // Create a new input tensor
+ Dims4 dims(InputN, InputC, InputH, InputW);
+ return Network->addInput("", dims);
+
+ }
+
+ return getIntermediateInputTensor(N);
}
+
unsigned CGT_NVDLA::getInputIndex(DFLeafNode* N, const IntrinsicInst *II) {
DEBUG(errs() << "GET INPUT INDEX\n");
auto *F = N->getFuncPointer();
@@ -661,8 +704,11 @@ std::string CGT_NVDLA::getLayerName(std::string Name) {
return std::to_string(LayerNameMap[Name]);
}
+
+
void CGT_NVDLA::generateConvolutionLayer(DFLeafNode* N, const IntrinsicInst *II) {
- DEBUG(errs() << "*****CONVOLUTION LAYER*****\n");
+
+ DEBUG(errs() << "*****CONVOLUTION LAYER*****\n");
// FIXME: What is number of "groups". Setting it to 1 for now.
int numGroups = 1;
@@ -707,18 +753,22 @@ void CGT_NVDLA::generateConvolutionLayer(DFLeafNode* N, const IntrinsicInst *II)
int kernelN = KernelNConst->getZExtValue();
DEBUG(errs() << "\nKERNEL H: " << kernelH << "\n");
DEBUG(errs() << "KERNEL W: " << kernelW << "\n");
- DEBUG(errs() << "KERNEL C: " << kernelC << "\n");
+ DEBUG(errs() << "KERNEL C: " << kernelC << "\n");
DEBUG(errs() << "KERNEL N: " << kernelN << "\n");
- int numOutputs;
+ int numOutputs;
if(!InputTensor) {
DEBUG(errs() << "INPUT FROM EDGE\n");
numOutputs = (InputNVDLATensor->getDimensions()).n * kernelN;
// (InputNVDLATensor->getDimensions()).c;
} else {
DEBUG(errs() << "INPUT FROM WEIGHT TENSOR\n");
- auto *BatchesConst = dyn_cast<ConstantInt>(InputTensor->getOperand(2));
- auto *ChannelsConst = dyn_cast<ConstantInt>(InputTensor->getOperand(3));
+
+ // FIXME: Reasonable assumption for now to assume batch_size = 1
+ auto *BatchesConst = ConstantInt::get(Type::getInt32Ty(M.getContext()), 1);
+ auto *ChannelsConst = dyn_cast<ConstantInt>(InputTensor->getOperand(4));
+ //auto *BatchesConst = dyn_cast<ConstantInt>(InputTensor->getOperand(2));
+ //auto *ChannelsConst = dyn_cast<ConstantInt>(InputTensor->getOperand(3));
numOutputs = BatchesConst->getZExtValue() * kernelN;
// ChannelsConst->getZExtValue();
DEBUG(errs() << "NUM OUTPUTS: " << numOutputs << "\n");
@@ -816,14 +866,16 @@ void CGT_NVDLA::generateConvolutionLayer(DFLeafNode* N, const IntrinsicInst *II)
Dims2 kernelSize = Dims2(kernelH, kernelW);
auto *Layer = Network->addConvolution(InputNVDLATensor, numOutputs, 0,
- kernelSize, tlPadding, brPadding, stride, dilation,
- kernelWeights, biasWeights, biasMode, numGroups);
+ kernelSize, tlPadding, brPadding, stride, dilation,
+ kernelWeights, biasWeights, biasMode, numGroups);
if(AddOpNodes.size()) {
- auto *Node = AddOpNodes[0];
- mapOutputTensor(Node, Layer->getOutput(0));
- } else {
- mapOutputTensor(N, Layer->getOutput(0));
+ auto *Node = AddOpNodes[0];
+ mapOutputTensor(Node, Layer->getOutput(0));
+ }
+ else{
+ mapOutputTensor(N, Layer->getOutput(0));
}
+
Layer->setName((std::string("conv") + getLayerName(std::string("conv"))).c_str());
DEBUG(errs() << Layer->getName() << "\n");
}
--
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