Adjust random data ranges for Main Compliance to avoid FP inf and nan

POW - there are now 3 test sets to cover random ranges. Also added ROUND
mode to data generator to force integer exponent values.
LOG, EXP, RSQRT, REDUCE_SUM & FULLY_CONNECTED - have had their ranges
reduced for each test.
Fix generate library configuration defaults and checks.

Signed-off-by: Jeremy Johnson <jeremy.johnson@arm.com>
Change-Id: Ie5d3bd78f690cc787a2ca4eb9b4bd6808bd9238c

8 files changed, 355 insertions, 39 deletions

diff --git a/reference_model/src/generate/generate_dot_product.cc b/reference_model/src/generate/generate_dot_product.cc
index 4054472..c8a2b13 100644
--- a/reference_model/src/generate/generate_dot_product.cc
+++ b/reference_model/src/generate/generate_dot_product.cc
@@ -387,7 +387,12 @@ bool generateDotProduct(const GenerateConfig& cfg, void* data, size_t size)
     if (!generator)
     {
         WARNING("[Generator][DP] Requested generator could not be created!");
-        return 0;
+        return false;
+    }
+    if (cfg.dotProductInfo.ks <= 0)
+    {
+        WARNING("[Generator][DP] Invalid test set kernel size %d.", cfg.dotProductInfo.ks);
+        return false;
     }
 
     // Select which generator to use
diff --git a/reference_model/src/generate/generate_pseudo_random.cc b/reference_model/src/generate/generate_pseudo_random.cc
index d8d2288..b51424d 100644
--- a/reference_model/src/generate/generate_pseudo_random.cc
+++ b/reference_model/src/generate/generate_pseudo_random.cc
@@ -93,6 +93,7 @@ bool generateFP32(const TosaReference::GenerateConfig& cfg, void* data, size_t s
     const TosaReference::PseudoRandomInfo& prinfo = cfg.pseudoRandomInfo;
 
     PseudoRandomGeneratorFloat<float>* generator;
+    bool roundMode = prinfo.round;
 
     if (prinfo.range.size() == 2)
     {
@@ -117,6 +118,10 @@ bool generateFP32(const TosaReference::GenerateConfig& cfg, void* data, size_t s
             // Set every 4th value to 0 to enable better comparison testing
             a[t] = 0.f;
         }
+        else if (roundMode)
+        {
+            a[t] = std::roundf(a[t]);
+        }
     }
     return true;
 }
diff --git a/reference_model/src/generate/generate_utils.cc b/reference_model/src/generate/generate_utils.cc
index 1edc79d..58a3d33 100644
--- a/reference_model/src/generate/generate_utils.cc
+++ b/reference_model/src/generate/generate_utils.cc
@@ -116,6 +116,10 @@ void from_json(const nlohmann::json& j, PseudoRandomInfo& pseudoRandomInfo)
     {
         j.at("range").get_to(pseudoRandomInfo.range);
     }
+    if (j.contains("round"))
+    {
+        j.at("round").get_to(pseudoRandomInfo.round);
+    }
 }
 
 void from_json(const nlohmann::json& j, GenerateConfig& cfg)
@@ -126,10 +130,22 @@ void from_json(const nlohmann::json& j, GenerateConfig& cfg)
     j.at("input_pos").get_to(cfg.inputPos);
     j.at("op").get_to(cfg.opType);
     j.at("generator").get_to(cfg.generatorType);
+
+    // Set up defaults for dotProductInfo
+    cfg.dotProductInfo.s       = -1;
+    cfg.dotProductInfo.ks      = -1;
+    cfg.dotProductInfo.accType = DType_UNKNOWN;
+    cfg.dotProductInfo.kernel  = std::vector<int32_t>();
+    cfg.dotProductInfo.axis    = -1;
     if (j.contains("dot_product_info"))
     {
         j.at("dot_product_info").get_to(cfg.dotProductInfo);
     }
+
+    // Set up defaults for pseudoRandomInfo
+    cfg.pseudoRandomInfo.rngSeed = -1;
+    cfg.pseudoRandomInfo.range   = std::vector<std::string>();
+    cfg.pseudoRandomInfo.round   = false;
     if (j.contains("pseudo_random_info"))
     {
         j.at("pseudo_random_info").get_to(cfg.pseudoRandomInfo);
diff --git a/reference_model/src/generate/generate_utils.h b/reference_model/src/generate/generate_utils.h
index 8d0f654..f9ec713 100644
--- a/reference_model/src/generate/generate_utils.h
+++ b/reference_model/src/generate/generate_utils.h
@@ -62,6 +62,7 @@ struct PseudoRandomInfo
 
     int64_t rngSeed;
     std::vector<std::string> range;
+    bool round;
 };
 
 /// \brief Generator configuration
diff --git a/scripts/schemavalidation/datagen-config.schema.json b/scripts/schemavalidation/datagen-config.schema.json
index 68789f6..08d564b 100644
--- a/scripts/schemavalidation/datagen-config.schema.json
+++ b/scripts/schemavalidation/datagen-config.schema.json
@@ -69,6 +69,10 @@
                                         "description": "[low value, high value] as strings to allow ints and floats",
                                         "type": "string"
                                     }
+                                },
+                                "round": {
+                                    "type": "boolean",
+                                    "description": "force rounding of all values"
                                 }
                             },
                             "additionalProperties": false,
diff --git a/verif/conformance/test_select.py b/verif/conformance/test_select.py
index faefc85..cebdf62 100644
--- a/verif/conformance/test_select.py
+++ b/verif/conformance/test_select.py
@@ -125,8 +125,6 @@ class Operator:
     # Working set of param_names - updated for negative tests
     wks_param_names = None
 
-    COMPLIANCE_SETS = ("_s0", "_s1", "_s2", "_s3", "_s4", "_s5")
-
     def __init__(
         self,
         test_dir: Path,
@@ -260,13 +258,13 @@ class Operator:
                 if (not negative and "ERRORIF" not in str(path)) or (
                     negative and "ERRORIF" in str(path)
                 ):
-                    # Check for compliance test set paths
-                    suffix = path.name[-3:]
-                    if suffix in Operator.COMPLIANCE_SETS:
-                        if suffix != Operator.COMPLIANCE_SETS[0]:
-                            # Only return one of the test sets
-                            continue
-                        yield path.with_name(path.name[:-3])
+                    # Check for test set paths
+                    match = re.match(r"(.*)_s([0-9]+)", path.name)
+                    if match:
+                        if match.group(2) == "0":
+                            # Only return the truncated test name
+                            # of the first test of a set
+                            yield path.with_name(match.group(1))
                     else:
                         yield path
 
@@ -298,6 +296,23 @@ class Operator:
             params[param] = sorted(list(params[param]))
         return params
 
+    @staticmethod
+    def _get_test_set_paths(path):
+        """Expand a path to find all the test sets."""
+        s = 0
+        paths = []
+        # Have a bound for the maximum test sets
+        while s < 100:
+            set_path = path.with_name(f"{path.name}_s{s}")
+            if set_path.exists():
+                paths.append(set_path)
+            else:
+                if s == 0:
+                    logger.error(f"Could not find test set 0 - {str(set_path)}")
+                break
+            s += 1
+        return paths
+
     def select_tests(self):  # noqa: C901 (function too complex)
         """Generate the paths to the selected tests for this operator."""
         if not self.test_paths:
@@ -356,9 +371,9 @@ class Operator:
                 if path.exists():
                     yield path
                 else:
-                    # Compliance test series - expand to all sets
-                    for s in Operator.COMPLIANCE_SETS:
-                        yield path.with_name(f"{path.name}{s}")
+                    # Must be a test set - expand to all test sets
+                    for p in Operator._get_test_set_paths(path):
+                        yield p
 
         # search for tests that match any unused parameter values
         for n, path in enumerate(sorted(list(unused_paths))):
@@ -377,9 +392,9 @@ class Operator:
                     if path.exists():
                         yield path
                     else:
-                        # Compliance test series - expand to all sets
-                        for s in Operator.COMPLIANCE_SETS:
-                            yield path.with_name(f"{path.name}{s}")
+                        # Must be a test set - expand to all test sets
+                        for p in Operator._get_test_set_paths(path):
+                            yield p
                     break
 
         if not self.ignore_missing:
diff --git a/verif/generator/tosa_arg_gen.py b/verif/generator/tosa_arg_gen.py
index 9147605..3057963 100644
--- a/verif/generator/tosa_arg_gen.py
+++ b/verif/generator/tosa_arg_gen.py
@@ -635,17 +635,40 @@ class TosaTensorValuesGen:
         DType.BF16: (1 << 128) - (1 << (127 - 7)),
     }
 
+    # Default lowest normal values for random numbers
+    TVG_FLOAT_LOW_VALUE = {
+        DType.FP32: np.exp2(-126),
+        DType.FP16: np.exp2(-14),
+        DType.BF16: np.exp2(-126),
+    }
+
     @staticmethod
-    def _get_data_range(testGen, dtype, highValueLookup):
+    def _get_data_range(testGen, dtype, highValueLookup, lowValueLookup=None):
+        # Return a tuple of (low,high) data range values for the given data
+        # type using a combination of per operator table limits, data limits
+        # and user supplied ranges for FP numbers
         if dtype in highValueLookup:
-            data_range = testGen.getDTypeRange(dtype, high_inclusive=True)
+            type_range = testGen.getDTypeRange(dtype, high_inclusive=True)
             high_val = highValueLookup[dtype]
+            if lowValueLookup is not None and dtype in lowValueLookup:
+                low_val = lowValueLookup[dtype]
+            else:
+                low_val = -high_val
             # Set the values to something that won't produce infinity whilst
-            # respecting the default ranges if less than the high value
-            return [
-                max(-high_val, data_range[0]),
-                min(high_val, data_range[1]),
-            ]
+            # respecting the default ranges if more/less than the low/high
+            # values
+            data_range = (
+                max(low_val, type_range[0]),
+                min(high_val, type_range[1]),
+            )
+            if data_range[0] > data_range[1]:
+                # Invalid data range from low to high created due to user
+                # constraints revert to using internal ranges as they are
+                # known to work
+                msg = f"Using safe data range ({low_val} to {high_val}) instead of supplied ({type_range[0]} to {type_range[1]})"
+                warnings.warn(msg)
+                data_range = (low_val, high_val)
+            return data_range
         return None
 
     @staticmethod
@@ -664,9 +687,18 @@ class TosaTensorValuesGen:
             # Fall back to internal data gen when dealing with unsupported types or ops
             data_range = argsDict["data_range"] if "data_range" in argsDict else None
             for idx, info in enumerate(zip(shapeList, dtypeList)):
+                roundMode = False
                 shape, dtype = info
+                if "data_range_list" in argsDict:
+                    data_range = argsDict["data_range_list"][idx]["range"]
+                    roundMode = (
+                        "round" in argsDict["data_range_list"][idx]
+                        and argsDict["data_range_list"][idx]["round"] is True
+                    )
                 # Ignore lazy data gen option and create data array using any range limits
                 arr = testGen.getRandTensor(shape, dtype, data_range)
+                if roundMode:
+                    arr = np.round(arr)
                 if idx < pCount:
                     tens_ser_list.append(testGen.ser.addPlaceholder(shape, dtype, arr))
                 else:
@@ -699,7 +731,12 @@ class TosaTensorValuesGen:
                 info = {}
                 # TODO - generate seed for this generator based on test
                 info["rng_seed"] = 42
-                if "data_range" in argsDict:
+
+                if "data_range_list" in argsDict:
+                    data_range = argsDict["data_range_list"][idx]["range"]
+                    if "round" in argsDict["data_range_list"][idx]:
+                        info["round"] = argsDict["data_range_list"][idx]["round"]
+                elif "data_range" in argsDict:
                     data_range = argsDict["data_range"]
                 else:
                     data_range = testGen.getDTypeRange(
@@ -788,7 +825,7 @@ class TosaTensorValuesGen:
                 testGen, opName, dtypeList, shapeList, argsDict, error_name
             )
 
-    # Set the data range to half the largest value
+    # Set the ADD/SUB data range to half the largest value to avoid infinities
     TVG_FLOAT_HIGH_VALUE_ADDSUB = {
         DType.FP32: (TVG_FLOAT_HIGH_VALUE[DType.FP32] / 2),
         DType.FP16: (TVG_FLOAT_HIGH_VALUE[DType.FP16] / 2),
@@ -987,7 +1024,8 @@ class TosaTensorValuesGen:
                 testGen, opName, dtypeList, shapeList, argsDict, error_name
             )
 
-    # Set the data range to the square root of the largest value
+    # Set the MUL data range to the square root of the largest value
+    # to avoid infinities
     TVG_FLOAT_HIGH_VALUE_MUL = {
         DType.FP32: math.sqrt(TVG_FLOAT_HIGH_VALUE[DType.FP32]),
         DType.FP16: math.sqrt(TVG_FLOAT_HIGH_VALUE[DType.FP16]),
@@ -1167,7 +1205,8 @@ class TosaTensorValuesGen:
 
     @staticmethod
     def tvgReduceSum(testGen, opName, dtypeList, shapeList, argsDict, error_name=None):
-        if dtypeList[0] == DType.INT32:
+        dtype = dtypeList[0]
+        if dtype == DType.INT32:
             op = testGen.TOSA_OP_LIST[opName]
             pCount, cCount = op["operands"]
             assert (
@@ -1181,14 +1220,219 @@ class TosaTensorValuesGen:
             )
             tens_ser_list = []
             tens_ser_list.append(
-                testGen.ser.addPlaceholder(shapeList[0], dtypeList[0], values_arr)
+                testGen.ser.addPlaceholder(shapeList[0], dtype, values_arr)
             )
             return TosaTensorValuesGen.TVGInfo(tens_ser_list, None)
         else:
             # ERROR_IF or dot product floating point test
+            if (
+                error_name is None
+                and argsDict["dg_type"] != gtu.ComplianceMode.DOT_PRODUCT
+            ):
+                # Limit ranges for (non error & non compliance) tests by using
+                # values that can be summed on any axis to not hit infinity
+                highval_lookup = {
+                    dtype: TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE[dtype]
+                    / max(shapeList[0])
+                }
+                data_range = TosaTensorValuesGen._get_data_range(
+                    testGen, dtype, highval_lookup
+                )
+                assert data_range is not None
+                argsDict["data_range"] = data_range
+
+            return TosaTensorValuesGen.tvgLazyGenDefault(
+                testGen, opName, dtypeList, shapeList, argsDict, error_name
+            )
+
+    # Set the POW exponent high data range
+    TVG_FLOAT_HIGH_VALUE_POW_EXP = {
+        DType.FP32: 10.0,
+        DType.FP16: 10.0,
+        DType.BF16: 10.0,
+    }
+    # POW highest base value (within a safe margin of error) that can be raised
+    # to +ve exponent that doesn't become Infinity
+    TVG_FLOAT_HIGH_VALUE_POW_BASE = {
+        DType.FP32: math.floor(
+            math.pow(
+                TVG_FLOAT_HIGH_VALUE[DType.FP32],
+                1.0 / TVG_FLOAT_HIGH_VALUE_POW_EXP[DType.FP32],
+            )
+        ),
+        DType.FP16: math.floor(
+            math.pow(
+                TVG_FLOAT_HIGH_VALUE[DType.FP16],
+                1.0 / TVG_FLOAT_HIGH_VALUE_POW_EXP[DType.FP16],
+            )
+        ),
+        DType.BF16: math.floor(
+            math.pow(
+                TVG_FLOAT_HIGH_VALUE[DType.BF16],
+                1.0 / TVG_FLOAT_HIGH_VALUE_POW_EXP[DType.BF16],
+            )
+        ),
+    }
+    # POW lowest base value (within a safe margin of error) that can be raised
+    # to -ve exponent that doesn't become Infinity
+    TVG_FLOAT_LOW_VALUE_POW_BASE = {
+        DType.FP32: math.ceil(
+            math.pow(
+                1.0 / TVG_FLOAT_HIGH_VALUE[DType.FP32],
+                1.0 / TVG_FLOAT_HIGH_VALUE_POW_EXP[DType.FP32],
+            )
+            * 1000
+        )
+        / 1000,
+        DType.FP16: math.ceil(
+            math.pow(
+                1.0 / TVG_FLOAT_HIGH_VALUE[DType.FP16],
+                1.0 / TVG_FLOAT_HIGH_VALUE_POW_EXP[DType.FP16],
+            )
+            * 1000
+        )
+        / 1000,
+        DType.BF16: math.ceil(
+            math.pow(
+                1.0 / TVG_FLOAT_HIGH_VALUE[DType.BF16],
+                1.0 / TVG_FLOAT_HIGH_VALUE_POW_EXP[DType.BF16],
+            )
+            * 1000
+        )
+        / 1000,
+    }
+
+    @staticmethod
+    def tvgPow(testGen, opName, dtypeList, shapeList, argsDict, error_name=None):
+        if error_name is not None:
             return TosaTensorValuesGen.tvgLazyGenDefault(
                 testGen, opName, dtypeList, shapeList, argsDict, error_name
             )
+        dtype = dtypeList[0]
+        # Different ranges for POW
+        test_set = argsDict["s"]
+        if test_set == 0:
+            # Positive base with fractional exponent
+            base_range = TosaTensorValuesGen._get_data_range(
+                testGen,
+                dtype,
+                TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_POW_BASE,
+                TosaTensorValuesGen.TVG_FLOAT_LOW_VALUE_POW_BASE,
+            )
+            exp_range = TosaTensorValuesGen._get_data_range(
+                testGen, dtype, TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_POW_EXP
+            )
+            exp_round = False
+        else:
+            # Integer exponent
+            exp_range = TosaTensorValuesGen._get_data_range(
+                testGen, dtype, TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_POW_EXP
+            )
+            exp_round = True
+            if test_set == 1:
+                # Positive base
+                base_range = TosaTensorValuesGen._get_data_range(
+                    testGen,
+                    dtype,
+                    TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_POW_BASE,
+                    TosaTensorValuesGen.TVG_FLOAT_LOW_VALUE_POW_BASE,
+                )
+            else:
+                assert test_set == 2
+                # Negative base
+                # Supply new look up tables with negative values
+                base_range = TosaTensorValuesGen._get_data_range(
+                    testGen,
+                    dtype,
+                    {dtype: -TosaTensorValuesGen.TVG_FLOAT_LOW_VALUE_POW_BASE[dtype]},
+                    {dtype: -TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_POW_BASE[dtype]},
+                )
+
+        data_range_list = (
+            {
+                "range": base_range,
+            },
+            {
+                "range": exp_range,
+                "round": exp_round,
+            },
+        )
+        argsDict["data_range_list"] = data_range_list
+        return TosaTensorValuesGen.tvgLazyGenDefault(
+            testGen, opName, dtypeList, shapeList, argsDict, error_name
+        )
+
+    @staticmethod
+    def tvgLogRsqrt(testGen, opName, dtypeList, shapeList, argsDict, error_name=None):
+        # LOG & RSQRT data range from lowest expressible positive number to
+        # largest to avoid NaNs
+        data_range = TosaTensorValuesGen._get_data_range(
+            testGen,
+            dtypeList[0],
+            TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE,
+            TosaTensorValuesGen.TVG_FLOAT_LOW_VALUE,
+        )
+        if data_range:
+            argsDict["data_range"] = data_range
+
+        return TosaTensorValuesGen.tvgLazyGenDefault(
+            testGen, opName, dtypeList, shapeList, argsDict, error_name
+        )
+
+    # Set the EXP data range to the log of the largest to smallest values
+    # to avoid infinities or making the result zero
+    TVG_FLOAT_HIGH_VALUE_EXP = {
+        DType.FP32: math.log(TVG_FLOAT_HIGH_VALUE[DType.FP32]),
+        DType.FP16: math.log(TVG_FLOAT_HIGH_VALUE[DType.FP16]),
+        DType.BF16: math.log(TVG_FLOAT_HIGH_VALUE[DType.BF16]),
+    }
+    TVG_FLOAT_LOW_VALUE_EXP = {
+        DType.FP32: math.log(TVG_FLOAT_LOW_VALUE[DType.FP32]),
+        DType.FP16: math.log(TVG_FLOAT_LOW_VALUE[DType.FP16]),
+        DType.BF16: math.log(TVG_FLOAT_LOW_VALUE[DType.BF16]),
+    }
+
+    @staticmethod
+    def tvgExp(testGen, opName, dtypeList, shapeList, argsDict, error_name=None):
+        data_range = TosaTensorValuesGen._get_data_range(
+            testGen,
+            dtypeList[0],
+            TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_EXP,
+            TosaTensorValuesGen.TVG_FLOAT_LOW_VALUE_EXP,
+        )
+        if data_range:
+            argsDict["data_range"] = data_range
+
+        return TosaTensorValuesGen.tvgLazyGenDefault(
+            testGen, opName, dtypeList, shapeList, argsDict, error_name
+        )
+
+    @staticmethod
+    def tvgFullyConnected(
+        testGen, opName, dtypeList, shapeList, argsDict, error_name=None
+    ):
+        dtype = dtypeList[0]
+        if (
+            error_name is None
+            and argsDict["dg_type"] != gtu.ComplianceMode.DOT_PRODUCT
+            and dtype in (DType.FP16, DType.BF16)
+        ):
+            # TODO - Remove once FP16 and BF16 enabled for DOT_PRODUCT compliance
+            # Limit ranges for (non error & non compliance) FP tests by using
+            # values that can be multiplied on any axis to not hit infinity/NaN
+            IC = shapeList[0][1]
+            highval_lookup = {
+                dtype: math.pow(TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE[dtype], 1 / IC)
+            }
+            data_range = TosaTensorValuesGen._get_data_range(
+                testGen, dtype, highval_lookup
+            )
+            assert data_range is not None
+            argsDict["data_range"] = data_range
+
+        return TosaTensorValuesGen.tvgLazyGenDefault(
+            testGen, opName, dtypeList, shapeList, argsDict, error_name
+        )
 
 
 class TosaArgGen:
@@ -1225,8 +1469,14 @@ class TosaArgGen:
             for arg_str, args_dict in arg_list:
                 args_dict["dg_type"] = dg_type
                 if dg_type == gtu.DataGenType.PSEUDO_RANDOM:
-                    # Default test
-                    new_arg_list.append((arg_str, args_dict))
+                    if error_name is None:
+                        num_test_sets = (
+                            args_dict["num_test_sets"]
+                            if "num_test_sets" in args_dict
+                            else 0
+                        )
+                    else:
+                        num_test_sets = 0
 
                 elif dg_type == gtu.DataGenType.DOT_PRODUCT:
                     # Extra tests for each dot product test set
@@ -1245,11 +1495,17 @@ class TosaArgGen:
                     assert "ks" in args_dict
                     assert "acc_type" in args_dict
 
-                    for s in testGen.TOSA_MI_DOT_PRODUCT_TEST_SETS:
-                        new_arg_str = f"{arg_str}_s{s}"
+                    num_test_sets = testGen.TOSA_MI_DOT_PRODUCT_TEST_SETS
+
+                if num_test_sets > 0:
+                    for s in range(0, num_test_sets):
+                        new_arg_str = f"{arg_str}_s{s}" if arg_str else f"s{s}"
                         new_args_dict = args_dict.copy()
                         new_args_dict["s"] = s
                         new_arg_list.append((new_arg_str, new_args_dict))
+                else:
+                    # Default is a single test
+                    new_arg_list.append((arg_str, args_dict))
 
         return new_arg_list
 
@@ -1268,6 +1524,20 @@ class TosaArgGen:
         return arg_list
 
     @staticmethod
+    def agPow(testGen, opName, shapeList, dtype, error_name=None):
+        """Pow operator needs different test sets to cover random numbers
+        without creating NaNs or Infs"""
+        arg_list = TosaArgGen._add_data_generators(
+            testGen,
+            opName,
+            dtype,
+            [("", {"num_test_sets": 3})],
+            error_name,
+        )
+        # Return list of tuples: (arg_str, args_dict)
+        return arg_list
+
+    @staticmethod
     def agAxis(testGen, opName, shapeList, dtype, error_name=None):
         """Build the axis argument for operators that take a single axis"""
         arg_list = []
diff --git a/verif/generator/tosa_test_gen.py b/verif/generator/tosa_test_gen.py
index 7b44ced..63958a9 100644
--- a/verif/generator/tosa_test_gen.py
+++ b/verif/generator/tosa_test_gen.py
@@ -37,7 +37,7 @@ class TosaTestGen:
     TOSA_8K_LEVEL_MAX_STRIDE = 8192
 
     # Main compliance dot product statistical test range
-    TOSA_MI_DOT_PRODUCT_TEST_SETS = range(0, 6)
+    TOSA_MI_DOT_PRODUCT_TEST_SETS = 6
     TOSA_MI_DOT_PRODUCT_MIN = 1000
 
     def __init__(self, args):
@@ -3074,7 +3074,7 @@ class TosaTestGen:
             "build_fcn": (
                 build_fully_connected,
                 TosaTensorGen.tgFullyConnected,
-                TosaTensorValuesGen.tvgLazyGenDefault,
+                TosaTensorValuesGen.tvgFullyConnected,
                 TosaArgGen.agFullyConnected,
             ),
             "qgen": TosaQuantGen.qgConv,
@@ -3562,8 +3562,8 @@ class TosaTestGen:
             "build_fcn": (
                 build_binary_broadcast,
                 TosaTensorGen.tgBroadcastFuzz,
-                TosaTensorValuesGen.tvgLazyGenDefault,
-                TosaArgGen.agNone,
+                TosaTensorValuesGen.tvgPow,
+                TosaArgGen.agPow,
             ),
             "types": TYPE_FP,
             "error_if_validators": (
@@ -3705,7 +3705,7 @@ class TosaTestGen:
             "build_fcn": (
                 build_unary,
                 TosaTensorGen.tgBasic,
-                TosaTensorValuesGen.tvgLazyGenDefault,
+                TosaTensorValuesGen.tvgExp,
                 TosaArgGen.agNone,
             ),
             "types": TYPE_FP,
@@ -3746,7 +3746,7 @@ class TosaTestGen:
             "build_fcn": (
                 build_unary,
                 TosaTensorGen.tgBasic,
-                TosaTensorValuesGen.tvgLazyGenDefault,
+                TosaTensorValuesGen.tvgLogRsqrt,
                 TosaArgGen.agNone,
             ),
             "types": TYPE_FP,
@@ -3828,7 +3828,7 @@ class TosaTestGen:
             "build_fcn": (
                 build_unary,
                 TosaTensorGen.tgBasic,
-                TosaTensorValuesGen.tvgLazyGenDefault,
+                TosaTensorValuesGen.tvgLogRsqrt,
                 TosaArgGen.agNone,
             ),
             "types": TYPE_FP,