Main Compliance testing for simple UNARY ops

For RECIPROCAL, RSQRT, CEIL, FLOOR, ABS, NEGATE & IDENTITY.
Improved ULP informational output.

Signed-off-by: Jeremy Johnson <jeremy.johnson@arm.com>
Change-Id: I49644573b4c9a30b2b9d6c9624f2a1d46976a378

9 files changed, 179 insertions, 77 deletions

diff --git a/reference_model/src/generate/generate_utils.cc b/reference_model/src/generate/generate_utils.cc
index c889f7b..fd42f21 100644
--- a/reference_model/src/generate/generate_utils.cc
+++ b/reference_model/src/generate/generate_utils.cc
@@ -38,16 +38,23 @@ NLOHMANN_JSON_SERIALIZE_ENUM(DType,
 NLOHMANN_JSON_SERIALIZE_ENUM(Op,
                              {
                                  { Op::Op_UNKNOWN, "UNKNOWN" },
+                                 { Op::Op_ABS, "ABS" },
                                  { Op::Op_ADD, "ADD" },
                                  { Op::Op_ARGMAX, "ARGMAX" },
+                                 { Op::Op_CEIL, "CEIL" },
                                  { Op::Op_CONV2D, "CONV2D" },
+                                 { Op::Op_FLOOR, "FLOOR" },
                                  { Op::Op_FULLY_CONNECTED, "FULLY_CONNECTED" },
+                                 { Op::Op_IDENTITY, "IDENTITY" },
                                  { Op::Op_MATMUL, "MATMUL" },
                                  { Op::Op_MAXIMUM, "MAXIMUM" },
                                  { Op::Op_MAX_POOL2D, "MAX_POOL2D" },
                                  { Op::Op_MINIMUM, "MINIMUM" },
                                  { Op::Op_MUL, "MUL" },
+                                 { Op::Op_NEGATE, "NEGATE" },
                                  { Op::Op_PAD, "PAD" },
+                                 { Op::Op_RECIPROCAL, "RECIPROCAL" },
+                                 { Op::Op_RSQRT, "RSQRT" },
                                  { Op::Op_REDUCE_MAX, "REDUCE_MAX" },
                                  { Op::Op_REDUCE_MIN, "REDUCE_MIN" },
                                  { Op::Op_REDUCE_SUM, "REDUCE_SUM" },
diff --git a/reference_model/src/verify/verify_ulp.cc b/reference_model/src/verify/verify_ulp.cc
index 8c27191..2af0012 100644
--- a/reference_model/src/verify/verify_ulp.cc
+++ b/reference_model/src/verify/verify_ulp.cc
@@ -16,6 +16,7 @@
 #include <limits>
 #include <memory>
 #include <type_traits>
+#include <utility>
 
 #include "verifiers.h"
 
@@ -36,9 +37,14 @@ bool tosaCheckULP(double referenceValue, float testValue, double ulpNum)
 
     // Start by sanitizing the input.
 
-    // The concept of ULP isn't defined for NaN's
+    // Both must be NaNs to be correct
     if (std::isnan(referenceValue) || std::isnan(testValue))
     {
+        if (std::isnan(referenceValue) && std::isnan(testValue))
+        {
+            return true;
+        }
+        WARNING("[Verfier][ULP] Non-matching NaN values - ref (%10f) versus test (%10f).", referenceValue, testValue);
         return false;
     }
 
@@ -112,8 +118,8 @@ bool tosaCheckULP(double referenceValue, float testValue, double ulpNum)
     bool withinUlp     = testValue64 >= referenceMin && testValue64 <= referenceMax;
     if (!withinUlp)
     {
-        WARNING("[Verfier][ULP] value (%10f) is not in ULP %g range (%10f <= ref (%10f) <= %10f).", testValue64, ulpNum,
-                referenceMin, referenceValue, referenceMax);
+        WARNING("[Verfier][ULP] value (%10.10f) is not in ULP %g range (%10.10f <= ref (%10.10f) <= %10.10f).",
+                testValue64, ulpNum, referenceMin, referenceValue, referenceMax);
     }
     return withinUlp;
 }
@@ -126,8 +132,8 @@ bool verifyULP(const CTensor* referenceTensor, const CTensor* implementationTens
     TOSA_REF_REQUIRE(implementationTensor != nullptr, "[ULP] Implementation tensor is missing");
 
     // Get number of elements
-    const auto elementCount =
-        numElements(std::vector<int32_t>(referenceTensor->shape, referenceTensor->shape + referenceTensor->num_dims));
+    const std::vector<int32_t> refShape(referenceTensor->shape, referenceTensor->shape + referenceTensor->num_dims);
+    const auto elementCount = numElements(refShape);
     TOSA_REF_REQUIRE(elementCount > 0, "[ULP] Invalid shape for reference tensor");
 
     const double ulp = ulpInfo.ulp;
@@ -138,10 +144,23 @@ bool verifyULP(const CTensor* referenceTensor, const CTensor* implementationTens
             TOSA_REF_REQUIRE(refData != nullptr, "[ULP] Missing data for reference");
             const auto* impData = reinterpret_cast<const float*>(implementationTensor->data);
             TOSA_REF_REQUIRE(impData != nullptr, "[ULP] Missing data for implementation");
-            return std::equal(refData, std::next(refData, elementCount), impData, std::next(impData, elementCount),
-                              [ulp](const auto& referenceValue, const auto& implementationValue) {
-                                  return tosaCheckULP(referenceValue, implementationValue, ulp);
-                              });
+            const auto* refDataEnd = std::next(refData, elementCount);
+            // Use mismatch to get the location of the first unequal value
+            auto pair = std::mismatch(refData, refDataEnd, impData, std::next(impData, elementCount),
+                                      [ulp](const auto& referenceValue, const auto& implementationValue) {
+                                          return tosaCheckULP(referenceValue, implementationValue, ulp);
+                                      });
+            if (std::get<0>(pair) == refDataEnd)
+            {
+                // No mismatch found
+                return true;
+            }
+            else
+            {
+                auto pos = indexToPosition(std::get<0>(pair) - refData, refShape);
+                WARNING("[Verfier][ULP] Location %s", positionToString(pos).c_str());
+                return false;
+            }
         }
         default:
             WARNING("[Verifier][ULP] Data-type not supported.");
diff --git a/reference_model/src/verify/verify_utils.cc b/reference_model/src/verify/verify_utils.cc
index 99cb0c1..9b20fb2 100644
--- a/reference_model/src/verify/verify_utils.cc
+++ b/reference_model/src/verify/verify_utils.cc
@@ -121,6 +121,32 @@ int64_t numElements(const std::vector<int32_t>& shape)
     return std::accumulate(std::begin(shape), std::end(shape), 1, std::multiplies<int64_t>());
 }
 
+std::vector<int32_t> indexToPosition(int64_t index, const std::vector<int32_t>& shape)
+{
+    std::vector<int32_t> pos;
+    for (auto d = shape.end() - 1; d >= shape.begin(); --d)
+    {
+        pos.insert(pos.begin(), index % *d);
+        index /= *d;
+    }
+    return pos;
+}
+
+std::string positionToString(const std::vector<int32_t>& pos)
+{
+    std::string str = "[";
+    for (auto d = pos.begin(); d < pos.end(); ++d)
+    {
+        str.append(std::to_string(*d));
+        if (pos.end() - d > 1)
+        {
+            str.append(",");
+        }
+    }
+    str.append("]");
+    return str;
+}
+
 DType mapToDType(tosa_datatype_t dataType)
 {
     static std::map<tosa_datatype_t, DType> typeMap = {
diff --git a/reference_model/src/verify/verify_utils.h b/reference_model/src/verify/verify_utils.h
index 15d7ba5..24d65b0 100644
--- a/reference_model/src/verify/verify_utils.h
+++ b/reference_model/src/verify/verify_utils.h
@@ -91,6 +91,12 @@ std::optional<VerifyConfig> parseVerifyConfig(const char* tensorName, const char
 /// \brief Extract number of total elements
 int64_t numElements(const std::vector<int32_t>& shape);
 
+/// \brief Convert a flat index to a shape position
+std::vector<int32_t> indexToPosition(int64_t index, const std::vector<int32_t>& shape);
+
+/// \brief A string representing the shape or position
+std::string positionToString(const std::vector<int32_t>& pos);
+
 /// \brief Map API data-type to DType
 DType mapToDType(tosa_datatype_t dataType);
 
diff --git a/reference_model/test/verify_tests.cpp b/reference_model/test/verify_tests.cpp
index e7d6c4e..31e27c0 100644
--- a/reference_model/test/verify_tests.cpp
+++ b/reference_model/test/verify_tests.cpp
@@ -392,7 +392,7 @@ TEST_CASE("positive - ulp")
     const auto elementCount = std::accumulate(std::begin(shape), std::end(shape), 1, std::multiplies<>());
 
     // Generate some random floats using the full range of fp32.
-    auto data_fp32 = generateRandomTensorData<float>(elementCount, false);
+    auto data_fp32 = generateRandomTensorData<float>(elementCount, true);
     std::vector<double> data_fp64(data_fp32.begin(), data_fp32.end());
 
     SUBCASE("same")
@@ -400,7 +400,7 @@ TEST_CASE("positive - ulp")
         // Generate some data that meets the ULP requirements of the result.
         auto otherData_fp32 = data_fp32;
         std::for_each(std::begin(otherData_fp32), std::end(otherData_fp32), [](auto& value) {
-            if (std::abs(value) != 0.0 && !std::isinf(value))
+            if (std::abs(value) != 0.0 && !std::isinf(value) && !std::isnan(value))
                 value = increment(value, 5);
         });
         const auto referenceTensor =
@@ -415,7 +415,7 @@ TEST_CASE("positive - ulp")
         // Generate some data that exceeds a specified number of ULP for each value in the tensor.
         auto otherData_fp32 = data_fp32;
         std::for_each(std::begin(otherData_fp32), std::end(otherData_fp32), [](auto& value) {
-            if (std::abs(value) != 0.0 && !std::isinf(value))
+            if (std::abs(value) != 0.0 && !std::isinf(value) && !std::isnan(value))
                 value = increment(value, 6);
         });
 
diff --git a/verif/conformance/tosa_main_profile_ops_info.json b/verif/conformance/tosa_main_profile_ops_info.json
index bdfc281..62505fd 100644
--- a/verif/conformance/tosa_main_profile_ops_info.json
+++ b/verif/conformance/tosa_main_profile_ops_info.json
@@ -4,6 +4,7 @@
         "profile": [
             "tosa-mi"
         ],
+        "support_for": [ "lazy_data_gen" ],
         "generation": {
             "standard": {
                 "generator_args": [
@@ -15,7 +16,7 @@
                         "--target-dtype",
                         "bf16",
                         "--fp-values-range",
-                        "-2.0,2.0",
+                        "-max,max",
                         "--tensor-dim-range",
                         "16,64",
                         "--target-rank",
@@ -33,7 +34,7 @@
                         "--target-dtype",
                         "bf16",
                         "--fp-values-range",
-                        "-2.0,2.0",
+                        "-max,max",
                         "--tensor-dim-range",
                         "1,16",
                         "--target-rank",
@@ -45,7 +46,7 @@
                         "--target-dtype",
                         "bf16",
                         "--fp-values-range",
-                        "-2.0,2.0",
+                        "-max,max",
                         "--target-shape",
                         "1,65531,2,1",
                         "--target-shape",
@@ -335,6 +336,7 @@
         "profile": [
             "tosa-mi"
         ],
+        "support_for": [ "lazy_data_gen" ],
         "generation": {
             "standard": {
                 "generator_args": [
@@ -346,7 +348,7 @@
                         "--target-dtype",
                         "bf16",
                         "--fp-values-range",
-                        "-2.0,2.0",
+                        "-max,max",
                         "--tensor-dim-range",
                         "4,64",
                         "--target-rank",
@@ -364,7 +366,7 @@
                         "--target-dtype",
                         "bf16",
                         "--fp-values-range",
-                        "-2.0,2.0",
+                        "-max,max",
                         "--tensor-dim-range",
                         "1,16",
                         "--target-rank",
@@ -376,7 +378,7 @@
                         "--target-dtype",
                         "fp16",
                         "--fp-values-range",
-                        "-2.0,2.0",
+                        "-max,max",
                         "--target-shape",
                         "2,1,65530,1",
                         "--target-shape",
@@ -1058,6 +1060,7 @@
         "profile": [
             "tosa-mi"
         ],
+        "support_for": [ "lazy_data_gen" ],
         "generation": {
             "standard": {
                 "generator_args": [
@@ -1069,7 +1072,7 @@
                         "--target-dtype",
                         "bf16",
                         "--fp-values-range",
-                        "-2.0,2.0",
+                        "-max,max",
                         "--tensor-dim-range",
                         "4,64",
                         "--target-rank",
@@ -1087,7 +1090,7 @@
                         "--target-dtype",
                         "bf16",
                         "--fp-values-range",
-                        "-2.0,2.0",
+                        "-max,max",
                         "--tensor-dim-range",
                         "1,16",
                         "--target-rank",
@@ -1099,7 +1102,7 @@
                         "--target-dtype",
                         "fp32",
                         "--fp-values-range",
-                        "-2.0,2.0",
+                        "-max,max",
                         "--target-shape",
                         "1,1,65533,2",
                         "--target-shape",
@@ -1297,6 +1300,7 @@
         "profile": [
             "tosa-mi"
         ],
+        "support_for": [ "lazy_data_gen" ],
         "generation": {
             "standard": {
                 "no_negative_tests": "true",
@@ -1309,7 +1313,7 @@
                         "--target-dtype",
                         "bf16",
                         "--fp-values-range",
-                        "-2.0,2.0",
+                        "-max,max",
                         "--tensor-dim-range",
                         "1,67",
                         "--target-rank",
@@ -1327,7 +1331,7 @@
                         "--target-dtype",
                         "bf16",
                         "--fp-values-range",
-                        "-2.0,2.0",
+                        "-max,max",
                         "--tensor-dim-range",
                         "1,19",
                         "--target-rank",
@@ -1339,7 +1343,7 @@
                         "--target-dtype",
                         "fp32",
                         "--fp-values-range",
-                        "-2.0,2.0",
+                        "-max,max",
                         "--target-shape",
                         "1,1,65539,1,1",
                         "--target-shape",
@@ -1546,6 +1550,7 @@
         "profile": [
             "tosa-mi"
         ],
+        "support_for": [ "lazy_data_gen" ],
         "generation": {
             "standard": {
                 "negative_dim_range": "1,10",
@@ -1558,7 +1563,7 @@
                         "--target-dtype",
                         "bf16",
                         "--fp-values-range",
-                        "-2.0,2.0",
+                        "-max,max",
                         "--tensor-dim-range",
                         "17,64",
                         "--target-rank",
@@ -1576,7 +1581,7 @@
                         "--target-dtype",
                         "bf16",
                         "--fp-values-range",
-                        "-2.0,2.0",
+                        "-max,max",
                         "--tensor-dim-range",
                         "1,22",
                         "--target-rank",
@@ -1588,7 +1593,7 @@
                         "--target-dtype",
                         "fp32",
                         "--fp-values-range",
-                        "-2.0,2.0",
+                        "-max,max",
                         "--target-shape",
                         "1,65541,3,1",
                         "--target-shape",
@@ -1761,6 +1766,7 @@
         "profile": [
             "tosa-mi"
         ],
+        "support_for": [ "lazy_data_gen" ],
         "generation": {
             "standard": {
                 "generator_args": [
@@ -1772,7 +1778,7 @@
                         "--target-dtype",
                         "bf16",
                         "--fp-values-range",
-                        "-2.0,2.0",
+                        "-max,max",
                         "--tensor-dim-range",
                         "15,64",
                         "--target-rank",
@@ -1786,7 +1792,7 @@
                         "--target-dtype",
                         "fp16",
                         "--fp-values-range",
-                        "-2.0,2.0",
+                        "-max,max",
                         "--tensor-dim-range",
                         "1,15",
                         "--target-rank",
@@ -1798,7 +1804,7 @@
                         "--target-dtype",
                         "bf16",
                         "--fp-values-range",
-                        "-2.0,2.0",
+                        "-max,max",
                         "--target-shape",
                         "1,1,65536,4",
                         "--target-shape",
@@ -1912,6 +1918,7 @@
         "profile": [
             "tosa-mi"
         ],
+        "support_for": [ "lazy_data_gen" ],
         "generation": {
             "standard": {
                 "generator_args": [
@@ -1923,7 +1930,7 @@
                         "--target-dtype",
                         "bf16",
                         "--fp-values-range",
-                        "-2.0,2.0",
+                        "-max,max",
                         "--tensor-dim-range",
                         "15,64",
                         "--target-rank",
@@ -1937,7 +1944,7 @@
                         "--target-dtype",
                         "bf16",
                         "--fp-values-range",
-                        "-2.0,2.0",
+                        "-max,max",
                         "--tensor-dim-range",
                         "1,15",
                         "--target-rank",
@@ -1949,7 +1956,7 @@
                         "--target-dtype",
                         "fp32",
                         "--fp-values-range",
-                        "-2.0,2.0",
+                        "-max,max",
                         "--target-shape",
                         "1,1,3,65541",
                         "--target-shape",
diff --git a/verif/generator/tosa_arg_gen.py b/verif/generator/tosa_arg_gen.py
index 1f54851..454013a 100644
--- a/verif/generator/tosa_arg_gen.py
+++ b/verif/generator/tosa_arg_gen.py
@@ -767,8 +767,10 @@ class TosaTensorValuesGen:
         return TosaTensorValuesGen.TVGInfo(tens_ser_list, tens_data)
 
     @staticmethod
-    def tvgNegate(testGen, op, dtypeList, shapeList, testArgs, error_name=None):
+    def tvgNegate(testGen, opName, dtypeList, shapeList, argsDict, error_name=None):
         if dtypeList[0] == DType.INT32 and error_name is None:
+            # Integer test
+            op = testGen.TOSA_OP_LIST[opName]
             pCount, cCount = op["operands"]
             assert (
                 pCount == 1 and cCount == 0
@@ -780,14 +782,15 @@ class TosaTensorValuesGen:
             arr = np.int32(
                 testGen.rng.integers(low=min_val, high=(max_val + 1), size=shapeList[0])
             )
-            placeholders = []
-            placeholders.append(
+            tens_ser_list = []
+            tens_ser_list.append(
                 testGen.ser.addPlaceholder(shapeList[0], dtypeList[0], arr)
             )
-            return placeholders
+            return TosaTensorValuesGen.TVGInfo(tens_ser_list, None)
         else:
-            return TosaTensorValuesGen.tvgDefault(
-                testGen, op, dtypeList, shapeList, testArgs, error_name
+            # ERROR_IF or floating point test
+            return TosaTensorValuesGen.tvgLazyGenDefault(
+                testGen, opName, dtypeList, shapeList, argsDict, error_name
             )
 
     # Set the data range to half the largest value
diff --git a/verif/generator/tosa_error_if.py b/verif/generator/tosa_error_if.py
index ed1a941..86be347 100644
--- a/verif/generator/tosa_error_if.py
+++ b/verif/generator/tosa_error_if.py
@@ -343,6 +343,9 @@ class TosaErrorValidator:
         Returns:
             True if the result matches the expected result; otherwise False
         """
+        if validator_fcns is None:
+            # Nothing to do
+            return True
         overall_result = True
         for val_fcn in validator_fcns:
             val_result = val_fcn(True, **kwargs)
diff --git a/verif/generator/tosa_test_gen.py b/verif/generator/tosa_test_gen.py
index d1fe11d..35cd78f 100644
--- a/verif/generator/tosa_test_gen.py
+++ b/verif/generator/tosa_test_gen.py
@@ -352,28 +352,26 @@ class TosaTestGen:
             self.resultTensor = resultTensor
             self.complianceDict = complianceDict
 
-    def build_unary(self, op, a, validator_fcns=None, error_name=None, qinfo=None):
-        result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, error_name)
+    def build_unary(
+        self, op, inputs, args_dict, validator_fcns=None, error_name=None, qinfo=None
+    ):
+        assert len(inputs) == 1
+        a = inputs[0]
+        result_tensor = OutputShaper.unaryOp(self.ser, self.rng, a, error_name)
 
-        # build_placeholder returns an int, ABS/other ops does not
-        if isinstance(op, int):
-            self.ser.addOperator(op, a.name, result_tens.name, None)
-            return result_tens
-        elif op["op"] == Op.IDENTITY:
-            self.ser.addOperator(op["op"], a.name, result_tens.name, None)
-            return result_tens
+        assert not isinstance(op, int)
 
         # Ensure new output type has correct qinfo
         if error_name == ErrorIf.WrongOutputType:
-            if result_tens.dtype not in [DType.INT8, DType.UINT8]:
+            if result_tensor.dtype not in [DType.INT8, DType.UINT8]:
                 qinfo = [
                     TosaQuantGen.getZeroPoint(self, a.dtype),
-                    TosaQuantGen.getZeroPoint(self, result_tens.dtype),
+                    TosaQuantGen.getZeroPoint(self, result_tensor.dtype),
                 ]
 
         # Invalidate Input/Output list for error if checks.
         input_list = [a.name]
-        output_list = [result_tens.name]
+        output_list = [result_tensor.name]
         pCount, cCount = op["operands"]
         num_operands = pCount + cCount
         input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
@@ -386,9 +384,9 @@ class TosaTestGen:
             error_name,
             op=op,
             input_dtype=a.dtype,
-            output_dtype=result_tens.dtype,
+            output_dtype=result_tensor.dtype,
             qinfo=qinfo,
-            result_tensors=[result_tens],
+            result_tensors=[result_tensor],
             input_list=input_list,
             output_list=output_list,
             num_operands=num_operands,
@@ -401,7 +399,15 @@ class TosaTestGen:
             attr.NegateAttribute(qinfo[0], qinfo[1])
 
         self.ser.addOperator(op["op"], input_list, output_list, attr)
-        return result_tens
+
+        if op["op"] in (Op.EXP, Op.LOG):
+            # TODO - add compliance support LOG and EXP
+            compliance = None
+        else:
+            compliance = self.tensorComplianceMetaData(
+                op, a.dtype, args_dict, result_tensor, error_name
+            )
+        return TosaTestGen.BuildInfo(result_tensor, compliance)
 
     def build_binary_broadcast(
         self, op, inputs, args_dict, validator_fcns, error_name=None, qinfo=None
@@ -3622,8 +3628,8 @@ class TosaTestGen:
             "build_fcn": (
                 build_unary,
                 TosaTensorGen.tgBasic,
-                TosaTensorValuesGen.tvgDefault,
-                None,
+                TosaTensorValuesGen.tvgLazyGenDefault,
+                TosaArgGen.agNone,
             ),
             "types": TYPE_FI32,
             "error_if_validators": (
@@ -3632,6 +3638,9 @@ class TosaTestGen:
                 TosaErrorValidator.evWrongInputList,
                 TosaErrorValidator.evWrongOutputList,
             ),
+            "data_gen": {
+                "fp": (gtu.DataGenType.PSEUDO_RANDOM,),
+            },
         },
         "bitwise_not": {
             "op": Op.BITWISE_NOT,
@@ -3639,8 +3648,8 @@ class TosaTestGen:
             "build_fcn": (
                 build_unary,
                 TosaTensorGen.tgBasic,
-                TosaTensorValuesGen.tvgDefault,
-                None,
+                TosaTensorValuesGen.tvgLazyGenDefault,
+                TosaArgGen.agNone,
             ),
             "types": TYPE_INT,
             "error_if_validators": (
@@ -3656,8 +3665,8 @@ class TosaTestGen:
             "build_fcn": (
                 build_unary,
                 TosaTensorGen.tgBasic,
-                TosaTensorValuesGen.tvgDefault,
-                None,
+                TosaTensorValuesGen.tvgLazyGenDefault,
+                TosaArgGen.agNone,
             ),
             "types": TYPE_FP,
             "error_if_validators": (
@@ -3666,6 +3675,10 @@ class TosaTestGen:
                 TosaErrorValidator.evWrongInputList,
                 TosaErrorValidator.evWrongOutputList,
             ),
+            "data_gen": {
+                "fp": (gtu.DataGenType.PSEUDO_RANDOM,),
+            },
+            "compliance": {"ulp": 0.5},
         },
         "clz": {
             "op": Op.CLZ,
@@ -3673,8 +3686,8 @@ class TosaTestGen:
             "build_fcn": (
                 build_unary,
                 TosaTensorGen.tgBasic,
-                TosaTensorValuesGen.tvgDefault,
-                None,
+                TosaTensorValuesGen.tvgLazyGenDefault,
+                TosaArgGen.agNone,
             ),
             "types": [DType.INT32],
             "error_if_validators": (
@@ -3690,8 +3703,8 @@ class TosaTestGen:
             "build_fcn": (
                 build_unary,
                 TosaTensorGen.tgBasic,
-                TosaTensorValuesGen.tvgDefault,
-                None,
+                TosaTensorValuesGen.tvgLazyGenDefault,
+                TosaArgGen.agNone,
             ),
             "types": TYPE_FP,
             "error_if_validators": (
@@ -3707,8 +3720,8 @@ class TosaTestGen:
             "build_fcn": (
                 build_unary,
                 TosaTensorGen.tgBasic,
-                TosaTensorValuesGen.tvgDefault,
-                None,
+                TosaTensorValuesGen.tvgLazyGenDefault,
+                TosaArgGen.agNone,
             ),
             "types": TYPE_FP,
             "error_if_validators": (
@@ -3717,6 +3730,10 @@ class TosaTestGen:
                 TosaErrorValidator.evWrongInputList,
                 TosaErrorValidator.evWrongOutputList,
             ),
+            "data_gen": {
+                "fp": (gtu.DataGenType.PSEUDO_RANDOM,),
+            },
+            "compliance": {"ulp": 0.5},
         },
         "log": {
             "op": Op.LOG,
@@ -3724,8 +3741,8 @@ class TosaTestGen:
             "build_fcn": (
                 build_unary,
                 TosaTensorGen.tgBasic,
-                TosaTensorValuesGen.tvgDefault,
-                None,
+                TosaTensorValuesGen.tvgLazyGenDefault,
+                TosaArgGen.agNone,
             ),
             "types": TYPE_FP,
             "error_if_validators": (
@@ -3741,8 +3758,8 @@ class TosaTestGen:
             "build_fcn": (
                 build_unary,
                 TosaTensorGen.tgBasic,
-                TosaTensorValuesGen.tvgDefault,
-                None,
+                TosaTensorValuesGen.tvgLazyGenDefault,
+                TosaArgGen.agNone,
             ),
             "types": TYPE_BOOL,
             "error_if_validators": (
@@ -3759,7 +3776,7 @@ class TosaTestGen:
                 build_unary,
                 TosaTensorGen.tgBasic,
                 TosaTensorValuesGen.tvgNegate,
-                None,
+                TosaArgGen.agNone,
             ),
             "qgen": TosaQuantGen.qgUnary,
             "types": TYPE_INT_FP,
@@ -3771,6 +3788,9 @@ class TosaTestGen:
                 TosaErrorValidator.evWrongInputList,
                 TosaErrorValidator.evWrongOutputList,
             ),
+            "data_gen": {
+                "fp": (gtu.DataGenType.PSEUDO_RANDOM,),
+            },
         },
         "reciprocal": {
             "op": Op.RECIPROCAL,
@@ -3778,8 +3798,8 @@ class TosaTestGen:
             "build_fcn": (
                 build_unary,
                 TosaTensorGen.tgBasic,
-                TosaTensorValuesGen.tvgDefault,
-                None,
+                TosaTensorValuesGen.tvgLazyGenDefault,
+                TosaArgGen.agNone,
             ),
             "types": TYPE_FP,
             "error_if_validators": (
@@ -3788,6 +3808,10 @@ class TosaTestGen:
                 TosaErrorValidator.evWrongInputList,
                 TosaErrorValidator.evWrongOutputList,
             ),
+            "data_gen": {
+                "fp": (gtu.DataGenType.PSEUDO_RANDOM,),
+            },
+            "compliance": {"ulp": 1.0},
         },
         "rsqrt": {
             "op": Op.RSQRT,
@@ -3795,8 +3819,8 @@ class TosaTestGen:
             "build_fcn": (
                 build_unary,
                 TosaTensorGen.tgBasic,
-                TosaTensorValuesGen.tvgDefault,
-                None,
+                TosaTensorValuesGen.tvgLazyGenDefault,
+                TosaArgGen.agNone,
             ),
             "types": TYPE_FP,
             "error_if_validators": (
@@ -3805,6 +3829,10 @@ class TosaTestGen:
                 TosaErrorValidator.evWrongInputList,
                 TosaErrorValidator.evWrongOutputList,
             ),
+            "data_gen": {
+                "fp": (gtu.DataGenType.PSEUDO_RANDOM,),
+            },
+            "compliance": {"ulp": 2},
         },
         # Elementwise Ternary operators
         "select": {
@@ -4220,10 +4248,13 @@ class TosaTestGen:
             "build_fcn": (
                 build_unary,
                 TosaTensorGen.tgBasic,
-                TosaTensorValuesGen.tvgDefault,
-                None,
+                TosaTensorValuesGen.tvgLazyGenDefault,
+                TosaArgGen.agNone,
             ),
             "types": TYPE_FIB,
+            "data_gen": {
+                "fp": (gtu.DataGenType.PSEUDO_RANDOM,),
+            },
         },
         # Scatter/Gather
         "gather": {