Update RESHAPE and TILE conformance testing

Signed-off-by: Won Jeon <won.jeon@arm.com>
Change-Id: Iaf59472cb32e03a92bad87ae9dba1b7548f20268

1 files changed, 60 insertions, 19 deletions

diff --git a/verif/generator/tosa_arg_gen.py b/verif/generator/tosa_arg_gen.py
index 00490fa..a655a50 100644
--- a/verif/generator/tosa_arg_gen.py
+++ b/verif/generator/tosa_arg_gen.py
@@ -731,7 +731,11 @@ class TosaTensorValuesGen:
                     # Change from inclusive to exclusive range
                     data_range = (data_range[0], data_range[1] + 1)
                 # Ignore lazy data gen option and create data array using any range limits
-                arr = testGen.getRandTensor(shape, dtype, data_range)
+
+                if "fixed_data" in argsDict and argsDict["fixed_data"][idx] is not None:
+                    arr = np.int64(argsDict["fixed_data"][idx])
+                else:
+                    arr = testGen.getRandTensor(shape, dtype, data_range)
                 if roundMode:
                     arr = np.round(arr)
                 if idx < pCount:
@@ -751,7 +755,13 @@ class TosaTensorValuesGen:
         for idx, shape in enumerate(shapeList):
 
             tens_meta = {}
-            tens_meta["generator"] = gtu.DataGenType(dg_type).name
+            if "fixed_data" in argsDict and argsDict["fixed_data"][idx] is not None:
+                tens_meta["generator"] = gtu.DataGenType(
+                    gtu.DataGenType.FIXED_DATA
+                ).name
+            else:
+                tens_meta["generator"] = gtu.DataGenType(dg_type).name
+
             tens_meta["data_type"] = gtu.DTYPE_ATTRIBUTES[dtypeList[idx]]["json"]
             tens_meta["shape"] = [int(i) for i in shape]
             tens_meta["input_pos"] = idx
@@ -764,23 +774,30 @@ class TosaTensorValuesGen:
 
             if dg_type == gtu.DataGenType.PSEUDO_RANDOM:
                 info = {}
-                # TODO - generate seed for this generator based on test
-                info["rng_seed"] = 42
-
-                data_range = None
-                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"]
-
-                if data_range is None:
-                    data_range = testGen.getDTypeRange(
-                        dtypeList[idx], high_inclusive=True
-                    )
-                info["range"] = [str(v) for v in data_range]
-                tens_meta["pseudo_random_info"] = info
+                if (
+                    tens_meta["generator"]
+                    == gtu.DataGenType(gtu.DataGenType.FIXED_DATA).name
+                ):
+                    info["data"] = [int(i) for i in argsDict["fixed_data"][idx]]
+                    tens_meta["fixed_data_info"] = info
+                else:
+                    # TODO - generate seed for this generator based on test
+                    info["rng_seed"] = 42
+
+                    data_range = None
+                    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"]
+
+                    if data_range is None:
+                        data_range = testGen.getDTypeRange(
+                            dtypeList[idx], high_inclusive=True
+                        )
+                    info["range"] = [str(v) for v in data_range]
+                    tens_meta["pseudo_random_info"] = info
             elif dg_type == gtu.DataGenType.DOT_PRODUCT:
                 info = {}
                 info["s"] = argsDict["s"]
@@ -812,6 +829,9 @@ class TosaTensorValuesGen:
                 dg_tens_meta[temp_name] = tens_meta
                 # Create data now using the temporary name to access meta details
                 data = testGen.dgl.get_tensor_data(temp_name, tens_data)
+                if tens_meta["data_type"] == "SHAPE":
+                    # Tensor type SHAPE and Numpy file type must be the same
+                    data = np.int64(data)
                 # Remove the item as we will give it the correct name later
                 del dg_tens_meta[temp_name]
 
@@ -1014,6 +1034,27 @@ class TosaTensorValuesGen:
         return placeholders
 
     @staticmethod
+    def tvgReshape(testGen, op, dtypeList, shapeList, argsDict, error_name=None):
+        dtypeList[1] = DType.SHAPE
+        shapeList[1] = [len(argsDict["new_shape"])]
+        # Create a new list for the pre-generated data in argsDict["fixed_data"]
+        argsDict["fixed_data"] = [None, argsDict["new_shape"]]
+
+        return TosaTensorValuesGen.tvgLazyGenDefault(
+            testGen, op, dtypeList, shapeList, argsDict, error_name
+        )
+
+    @staticmethod
+    def tvgTile(testGen, op, dtypeList, shapeList, argsDict, error_name=None):
+        dtypeList[1] = DType.SHAPE
+        shapeList[1] = [len(argsDict["multiples"])]
+        argsDict["fixed_data"] = [None, argsDict["multiples"]]
+
+        return TosaTensorValuesGen.tvgLazyGenDefault(
+            testGen, op, dtypeList, shapeList, argsDict, error_name
+        )
+
+    @staticmethod
     def tvgSelect(testGen, opName, dtypeList, shapeList, argsDict, error_name=None):
         # Set datatype of condition tensor to boolean
         dtypeList[0] = DType.BOOL