From 7f0ba9f9384eda28aacb6d03f79c61285bc9f9c6 Mon Sep 17 00:00:00 2001
From: Hashim Sharif <hsharif3@tyler.cs.illinois.edu>
Date: Fri, 17 Jul 2020 15:56:17 -0500
Subject: [PATCH] Updating unit tessts
---
.../dnn_sources/src/unit_tests.cc | 58 +++++++++++++------
1 file changed, 39 insertions(+), 19 deletions(-)
diff --git a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/unit_tests.cc b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/unit_tests.cc
index bde9a0cace..dd0abdb9c6 100644
--- a/llvm/projects/hpvm-tensor-rt/dnn_sources/src/unit_tests.cc
+++ b/llvm/projects/hpvm-tensor-rt/dnn_sources/src/unit_tests.cc
@@ -62,24 +62,23 @@ void testTensorHgemm(){
-void testTensorSgemm2(){
+void testTensorSgemm(){
printf("***** TensorSgemm ***** \n\n");
- void* lhs_ptr = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW,
- 10000, 800, 1, 1);
+ void* lhs_ptr = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 5, 4, 1, 1);
struct Tensor* lhs = (struct Tensor*) lhs_ptr;
-
+ fillTensorWithOnes(lhs);
+
float* data_arr = (float*) lhs->host_data;
for(int i = 0; i < lhs->num_elems; i++){
data_arr[i] = (i / 4) + 1;
}
-
- void* rhs = create4DTensor(CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT,
- 1, 1, 800, 800);
+
+ void* rhs = create4DTensor(CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, 1, 4, 3);
fillTensorWithOnes(rhs);
void* output = tensorGemmGPU(lhs, rhs);
- //printTensorValues(output);
+ printTensorValues(output);
}
@@ -291,24 +290,42 @@ void testTensorError(){
void testTensorConv(){
- // NOTE: The input channel count value (param2 to Tensor and Filter) must be the same
- void* x3 = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 1, 2, 4, 4);
- // NOTE: Filter descriptors do NOT have batch size
- // NOTE: First two dims are output channels (configurable), input channels (MUST match input channels)
- void* filter = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 2, 2, 2, 2);
- fillTensorWithOnes(x3);
+ void* input = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 1, 2, 4, 4);
+ void* filter = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 2, 2, 3, 3);
+
+ fillTensorWithOnes(input);
fillTensorWithOnes(filter);
int conv_mode = 1; // NOTE: uses CROSS_CORRELATION
int compute_precision = 0; // floating point precision for conv
- void* conv1 = tensorConvolution(x3, filter, 0, 0,
+ void* conv_out = tensorConvolution(input, filter, 0, 0,
1, 1, conv_mode, compute_precision);
- printTensorValues(conv1);
+ printTensorValues(conv_out);
}
+void testTensorHalfConv(){
+
+ void* input = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 1, 2, 4, 4);
+ void* filter = create4DTensor(CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 2, 2, 3, 3);
+
+ fillTensorWithOnes(input);
+ fillTensorWithOnes(filter);
+
+ int conv_mode = 1; // NOTE: uses CROSS_CORRELATION
+ int compute_precision = 0; // floating point precision for conv
+
+ void* conv_out = tensorHalfConvolution(input, filter, 0, 0,
+ 1, 1, conv_mode, compute_precision);
+ printTensorValues(conv_out);
+
+}
+
+
+
+
void testTensorGroupedConv(){
// NOTE: The input channel count value (param2 to Tensor and Filter) must be the same
@@ -1176,11 +1193,14 @@ int main(){
llvm_hpvm_initTensorRt(0);
startProfiling();
+
+ // Function call per unit test
+ testTensorHgemm();
+ testTensorSgemm();
- testTensorHgemm();
+ testTensorConv();
+ testTensorHalfConv();
- //testTensorSgemm2();
- //testTensorConv();
//testTensorError();
//testQuantization();
--
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