script level tests for relu & conv2d

tests are failing atm

UserConfig.json

@@ -13,7 +13,7 @@
     "use_mlir_codegen": false,
     "vectorized_single_queue": false,
     "debug_llvm": false,
-    "explain_kernels": true,
+    "explain_kernels": false,
     "explain_llvm": false,
     "explain_parsing": false,
     "explain_parsing_simplified": false,

scripts/examples/mnist_lenet-train.daph

@@ -76,16 +76,16 @@ import "nn/networks/lenet-train.daph" as "lenet";
 #fmt = ifdef($fmt, "csv")
 #train = read($train, format=fmt)
 #test = read($test, format=fmt)
-#train_X = readMatrix("./data/mnist20_features.csv");
-#train_Y = readMatrix("./data/mnist20_labels.csv");
+train_X = readMatrix("./data/mnist20_features.csv");
+train_Y = readMatrix("./data/mnist20_labels.csv");
 #test_X = readMatrix("./data/mnist20_features.csv");
 #test_Y = readMatrix("./data/mnist20_labels.csv");
 #C = $C
 #Hin = $Hin
 #Win = $Win
 
-train_X = readMatrix("./data/mnist/mnist_features.csv");
-train_Y = readMatrix("./data/mnist/mnist_labels.csv");
+#train_X = readMatrix("./data/mnist/mnist_features.csv");
+#train_Y = readMatrix("./data/mnist/mnist_labels.csv");
 #test_X = readMatrix("./data/mnist/mnist_features.csv");
 #test_Y = readMatrix("./data/mnist/mnist_labels.csv");
 
@@ -103,10 +103,14 @@ out_dir = ".";
 #X_test = test[,1:ncol(test)];
 #Y_test = test[,1];
 
-images = train_X[0:5000,];
-labels = train_Y[0:5000,];
-X_test = train_X[5001:6000,];
-Y_test = train_Y[5001:6000,];
+#images = train_X[0:5000,];
+#labels = train_Y[0:5000,];
+#X_test = train_X[5001:6000,];
+#Y_test = train_Y[5001:6000,];
+images = train_X[0:15,];
+labels = train_Y[0:15,];
+X_test = train_X[16:20,];
+Y_test = train_Y[16:20,];
 
 # Scale images to [-1,1], and one-hot encode the labels
 n = nrow(images);
@@ -124,18 +128,21 @@ X_test = (X_test / 255.0) * 2 - 1;
 #Y = labels[5001:nrow(images),];
 #Y_val = labels[1:5000,];
 #X = images[1001:nrow(images) - 3000,];
-X = images[1001:nrow(images) - 3000,];
-X_val = images[0:1000,];
-Y = labels[11:nrow(images),];
-Y_val = labels[0:1000,];
-
+#X = images[1001:nrow(images) - 3000,];
+#X_val = images[0:1000,];
+#Y = labels[11:nrow(images),];
+#Y_val = labels[0:1000,];
+X = images[0:nrow(images) - 3,];
+X_val = images[nrow(images) - 3:nrow(images),];
+Y = labels[0:nrow(images)-3,];
+Y_val = labels[nrow(images) - 3:nrow(images),];
 
 
 # Train
 W1, b1 = lenet.train(X, Y, X_val, Y_val, C, Hin, Win, epochs);
 
-#print(sum(as.matrix<f64>(W1)));
-#print(sum(b1));
+print(sum(as.matrix<f64>(W1)));
+print(sum(b1));
 # Write model out
 #mat_W1 = as.matrix<f64>(W1);
 write(W1, out_dir + "/W1.csv");

test/api/cli/nn/NNOperationsTest.cpp

@@ -34,24 +34,27 @@ const std::string expected_output = "diff: 0\n";
  * 3...kernel forward
  * 4...kernel backward
  */
+
+//                    const std::string expected_result = std::string("expected=\"") + dirPath + base_name + std::string(".csv\"");
+//                    const std::string args = "input=\"test/data/mnist20/mnist20_features.csv\",eps=1e-9f," + expected_result;
 #define MAKE_TEST_CASE(name, count) \
     TEST_CASE(name, TAG_DNN) { \
         for(unsigned i = 1; i <= count; i++) { \
             DYNAMIC_SECTION(name "_" << i << ".daph") { \
                     const std::string base_name = name + std::string("_") + std::to_string(i); \
                     const std::string script_name =  base_name + std::string(".daph"); \
-                    const std::string expected_result = std::string("expected=\"") + dirPath + base_name + std::string(".csv\""); \
-                    const std::string args = "input=\"test/data/mnist20/mnist20_features.csv\",eps=1e-9f," + expected_result; \
-                    compareDaphneToStr(expected_output, dirPath + script_name, "--config", "UserConfig.json", "--args", args.c_str()); \
+                    compareDaphneToStr(expected_output, dirPath + script_name, "--config", "test/api/cli/nn/UserConfig.json"); \
             } \
         } \
     }
 
-MAKE_TEST_CASE("activation_relu_dsl", 1)
+MAKE_TEST_CASE("activation_relu_dsl", 2)
+MAKE_TEST_CASE("conv2d_dsl", 2)
+MAKE_TEST_CASE("conv2d_kernel", 2)
+
 /*
 MAKE_TEST_CASE("affine_dsl", 1)
 MAKE_TEST_CASE("batchnorm2d_dsl", 1)
-MAKE_TEST_CASE("conv2d_dsl", 1)
 MAKE_TEST_CASE("dropout_dsl", 1)
 MAKE_TEST_CASE("loss_dsl", 1)
 MAKE_TEST_CASE("pooling", 1)

test/api/cli/nn/UserConfig.json

@@ -0,0 +1,81 @@
+{
+    "matmul_vec_size_bits": 0,
+    "matmul_tile": false,
+    "matmul_use_fixed_tile_sizes": true,
+    "matmul_fixed_tile_sizes": [4, 4, 4, 4, 4],
+    "matmul_unroll_factor": 1,
+    "matmul_unroll_jam_factor": 4,
+    "matmul_num_vec_registers": 16,
+    "use_cuda": false,
+    "use_vectorized_exec": false,
+    "use_obj_ref_mgnt": true,
+    "cuda_fuse_any": false,
+    "use_mlir_codegen": false,
+    "vectorized_single_queue": false,
+    "debug_llvm": false,
+    "explain_kernels": false,
+    "explain_llvm": false,
+    "explain_parsing": false,
+    "explain_parsing_simplified": false,
+    "explain_property_inference": false,
+    "explain_sql": false,
+    "explain_select_matrix_repr": false,
+    "explain_type_adaptation": false,
+    "explain_vectorized": false,
+    "explain_obj_ref_mgnt": false,
+    "explain_mlir_codegen": false,
+    "taskPartitioningScheme": "STATIC",
+    "numberOfThreads": -1,
+    "minimumTaskSize": 1,
+    "libdir": "{exedir}/../lib",
+    "daphnedsl_import_paths": {
+        "default_dirs": ["scripts/algorithms", "scripts"]
+    },
+    "force_cuda": true,
+    "logging": [
+        { "log-level-limit": "WARN" },
+        {
+            "comment": "This configuration controls logging in the GPU compiler pass only",
+            "name": "compiler::cuda",
+            "level": "INFO",
+            "filename": "compiler-trace-cuda.txt",
+            "format": "%^[%L %n]:%$ %v"
+        },
+        {
+            "comment": "general runtime CUDA debug log",
+            "name": "runtime::cuda",
+            "level": "DEBUG",
+            "filename": "compiler-debug-cuda.txt",
+            "format": "%^[%L %n]:%$ %v"
+        },
+        {
+            "comment": "DAPHNE runtime logs",
+            "name": "runtime",
+            "level": "DEBUG",
+            "filename": "",
+            "format": "%^[%n %L]:%$ %v"
+        },
+        {
+            "comment": "DAPHNE compiler logs",
+            "name": "compiler",
+            "level": "INFO",
+            "filename": "",
+            "format": "%^[%n %L]:%$ %v"
+        },
+        {
+            "comment": "DAPHNE default",
+            "name": "default",
+            "level": "DEBUG",
+            "filename": "",
+            "format": "%^[%n %L]:%$ %v"
+        },
+        {
+            "comment": "DAPHNE default",
+            "name": "parser",
+            "level": "DEBUG",
+            "filename": "",
+            "format": "%^[%n %L]:%$ %v"
+        }
+    ],
+    "sparsity_threshold": 0.25
+}

test/api/cli/nn/activation_relu_dsl_1.daph

@@ -1,20 +1,11 @@
-def compare_matrices(A:matrix, B:matrix, eps) -> si64 {
-
-  #diff = A - B;
-  #print(diff);
-  diff1 = sum(abs(A - B) > eps);
-  return diff1;
-}
 
 import "nn/layers/relu_dsl.daph" as "relu";
+import "./test_utils.daph" as "util";
 
-input = readMatrix($input);
-
-expected = readMatrix($expected);
+X = readMatrix("test/data/nn_validation/X.csv");
+expected = readMatrix("test/data/nn_validation/outR.csv");
 
-result = relu.forward(input);
-#writeMatrix(result, "relu_fwd.csv");
-diff = compare_matrices(result, expected, $eps);
+result = relu.forward(X);
 
+diff = util.compare_matrices(result, expected);
 print("diff: ", 0); print(diff);
-#print(result);

test/api/cli/nn/activation_relu_dsl_2.daph

@@ -0,0 +1,14 @@
+
+import "nn/layers/relu_dsl.daph" as "relu";
+import "./test_utils.daph" as "util";
+
+eps=1e-9f;
+
+X = readMatrix("test/data/nn_validation/X.csv");
+outR = readMatrix("test/data/nn_validation/outR.csv");
+expected = readMatrix("test/data/nn_validation/doutR.csv");
+
+result = relu.backward(outR, X);
+
+diff = util.compare_matrices(result, expected);
+print("diff: ", 0); print(diff);

test/api/cli/nn/conv2d_dsl_1.daph

@@ -0,0 +1,33 @@
+
+import "nn/layers/conv2d_dsl.daph" as "conv2d";
+import "./test_utils.daph" as "util";
+
+# number of "images"
+N=13;
+
+#image size
+Hin=13;
+Win=13;
+F=7;
+C=3;
+Hf=5;
+Wf=5;
+stride = 1;
+pad = 2;
+
+X = readMatrix("test/data/nn_validation/X.csv");
+W = readMatrix("test/data/nn_validation/WC.csv");
+b = readMatrix("test/data/nn_validation/bC.csv");
+expected = readMatrix("test/data/nn_validation/outC.csv");
+
+Hexp = sqrt(ncol(expected) / F);
+result, Hout, Wout = conv2d.forward(X, W, b, C, Hin, Win, Hf, Wf, stride, stride, pad, pad);
+
+if(Hexp != Hout) {
+//print("ncol exp / F: ",0);print(ncol(expected) / F);
+  print("Hexp: ",0); print(Hexp,0);
+  print("Hout: ",0); print(Hout,0);
+}
+
+diff = util.compare_matrices(result, expected);
+print("diff: ", 0); print(diff);

test/api/cli/nn/conv2d_dsl_2.daph

@@ -0,0 +1,32 @@
+
+import "nn/layers/conv2d_dsl.daph" as "conv2d";
+import "./test_utils.daph" as "util";
+
+# number of "images"
+N=13;
+
+#image size
+Hin=13;
+Win=13;
+F=7;
+C=3;
+Hf=5;
+Wf=5;
+stride = 1;
+pad = 2;
+
+X = readMatrix("test/data/nn_validation/X.csv");
+W = readMatrix("test/data/nn_validation/WC.csv");
+b = readMatrix("test/data/nn_validation/bC.csv");
+outC = readMatrix("test/data/nn_validation/outC.csv");
+Hout = sqrt(ncol(outC) / F);
+Wout = Hout;
+
+exp_dX = readMatrix("test/data/nn_validation/doutC.csv");
+exp_dW = readMatrix("test/data/nn_validation/dWC.csv");
+exp_db = readMatrix("test/data/nn_validation/dbC.csv");
+
+dX, dW, dB = conv2d.backward(outC, Hout, Wout, X, W, b, C, Hin, Win, Hf, Wf, stride, stride, pad, pad);
+
+diff = util.compare_matrices(dX, exp_dX) + util.compare_matrices(W, exp_dW) + util.compare_matrices(b, exp_db);
+print("diff: ", 0); print(diff);

test/api/cli/nn/conv2d_kernel_1.daph

@@ -0,0 +1,32 @@
+
+import "nn/layers/conv2d_kernel.daph" as "conv2d";
+import "./test_utils.daph" as "util";
+
+# number of "images"
+N=13;
+
+#image size
+Hin=13;
+Win=13;
+F=7;
+C=3;
+Hf=5;
+Wf=5;
+stride = 1;
+pad = 2;
+
+X = readMatrix("test/data/nn_validation/X.csv");
+W = readMatrix("test/data/nn_validation/WC.csv");
+b = readMatrix("test/data/nn_validation/bC.csv");
+expected = readMatrix("test/data/nn_validation/outC.csv");
+
+Hexp = sqrt(ncol(expected) / F);
+result, Hout, Wout = conv2d.forward(X, W, b, C, Hin, Win, Hf, Wf, stride, stride, pad, pad);
+
+if(Hexp != Hout) {
+  print("Hexp: ",0); print(Hexp,0);
+  print("Hout: ",0); print(Hout,0);
+}
+
+diff = util.compare_matrices(result, expected);
+print("diff: ", 0); print(diff);

test/api/cli/nn/conv2d_kernel_2.daph

@@ -0,0 +1,32 @@
+
+import "nn/layers/conv2d_kernel.daph" as "conv2d";
+import "./test_utils.daph" as "util";
+
+# number of "images"
+N=13;
+
+#image size
+Hin=13;
+Win=13;
+F=7;
+C=3;
+Hf=5;
+Wf=5;
+stride = 1;
+pad = 2;
+
+X = readMatrix("test/data/nn_validation/X.csv");
+W = readMatrix("test/data/nn_validation/WC.csv");
+b = readMatrix("test/data/nn_validation/bC.csv");
+outC = readMatrix("test/data/nn_validation/outC.csv");
+Hout = sqrt(ncol(outC) / F);
+Wout = Hout;
+
+exp_dX = readMatrix("test/data/nn_validation/doutC.csv");
+exp_dW = readMatrix("test/data/nn_validation/dWC.csv");
+exp_db = readMatrix("test/data/nn_validation/dbC.csv");
+
+dX, dW, dB = conv2d.backward(outC, Hout, Wout, X, W, b, C, Hin, Win, Hf, Wf, stride, stride, pad, pad);
+
+diff = util.compare_matrices(dX, exp_dX) + util.compare_matrices(W, exp_dW) + util.compare_matrices(b, exp_db);
+print("diff: ", 0); print(diff);

test/api/cli/nn/test_utils.daph

@@ -0,0 +1,8 @@
+
+def compare_matrices(A:matrix, B:matrix) -> si64 {
+    eps = 1e-9f;
+    diff1 = abs(A - B);
+    diff2 = diff1 > eps;
+    #print(diff1);
+    return sum(diff2);
+}