Add RFFT2d to the reference model

Includes:
* RFFT2d reference implementation
* TFLite framework tests
* Basic TOSA tests
* Serialization submodule upgrade with support for FFT/RFFT

Signed-off-by: Luke Hutton <luke.hutton@arm.com>
Change-Id: I2a687e9cf87fb62a26160ea52439ba9830bea36e

1 files changed, 73 insertions, 30 deletions

diff --git a/verif/generator/tosa_error_if.py b/verif/generator/tosa_error_if.py
index c9d35c7..40c5d13 100644
--- a/verif/generator/tosa_error_if.py
+++ b/verif/generator/tosa_error_if.py
@@ -1,5 +1,7 @@
-# Copyright (c) 2021-2022, ARM Limited.
+# Copyright (c) 2021-2023, ARM Limited.
 # SPDX-License-Identifier: Apache-2.0
+import math
+
 import numpy as np
 from generator.tosa_utils import MAX_RESIZE_DIMENSION
 from generator.tosa_utils import product
@@ -76,6 +78,7 @@ class ErrorIf(object):
     CondIfCondNotMatchingBool = "CondIfCondNotMatchingBool"
     CondIfCondShapeNotSizeOne = "CondIfCondShapeNotSizeOne"
     CondGraphOutputShapeNotSizeOne = "CondGraphOutputShapeNotSizeOne"
+    KernelNotPowerOfTwo = "KernelNotPowerOfTwo"
 
 
 class TosaErrorIfArgGen:
@@ -548,6 +551,10 @@ class TosaErrorValidator:
                 ):
                     error_result = True
 
+            elif op["op"] == Op.RFFT2D:
+                if not all([ty == input_dtype for ty in output_dtype]):
+                    error_result = True
+
             elif op["op"] in {
                 Op.CONV2D,
                 Op.CONV3D,
@@ -665,9 +672,13 @@ class TosaErrorValidator:
         error_reason = "Op output list does not match expected output"
 
         if check:
+            op = kwargs["op"]
             output_list = kwargs["output_list"]
-            # Note this will be incorrect if an operator returns more than one output
-            if len(output_list) != 1:
+            expected_length = 1
+            if op["op"] == Op.RFFT2D:
+                expected_length = 2
+
+            if len(output_list) != expected_length:
                 error_result = True
 
         info_dict = {
@@ -711,7 +722,7 @@ class TosaErrorValidator:
     @staticmethod
     def evBatchMismatch(check=False, **kwargs):
         error_name = ErrorIf.BatchMismatch
-        param_reqs = {"rank": [4, 4], "dtype": None, "shape": None}
+        param_reqs = {"rank": None, "dtype": None, "shape": None}
         error_result = False
         error_reason = "Input batch size not equal to output batch size"
 
@@ -722,12 +733,15 @@ class TosaErrorValidator:
 
         if check:
             input_shape = kwargs["input_shape"]
-            output_shape = kwargs[
-                "result_tensor"
-            ].shape  # Note this is just (N, OH, OW, C)
 
-            if (len(input_shape) in rank_range) and (input_shape[0] != output_shape[0]):
-                error_result = True
+            for output in kwargs["result_tensors"]:
+                output_shape = (
+                    output.shape
+                )  # Note batch is expected to be the first dim
+                if (len(input_shape) in rank_range) and (
+                    input_shape[0] != output_shape[0]
+                ):
+                    error_result = True
 
         info_dict = {
             "error_name": error_name,
@@ -751,11 +765,12 @@ class TosaErrorValidator:
 
         if check:
             input_shape = kwargs["input_shape"]
-            output_shape = kwargs[
-                "result_tensor"
-            ].shape  # Note this is just (N, OH, OW, C)
-            if (len(input_shape) in rank_range) and (input_shape[3] != output_shape[3]):
-                error_result = True
+            for output in kwargs["result_tensors"]:
+                output_shape = output.shape  # Note this is just (N, OH, OW, C)
+                if (len(input_shape) in rank_range) and (
+                    input_shape[3] != output_shape[3]
+                ):
+                    error_result = True
 
         info_dict = {
             "error_name": error_name,
@@ -1044,13 +1059,15 @@ class TosaErrorValidator:
             input3_shape = (
                 kwargs["input3"].shape if "input3" in kwargs else input2_shape
             )
-            output_shape = kwargs["result_tensor"].shape
-            if (
-                (len(input1_shape) != len(output_shape))
-                or (len(input2_shape) != len(output_shape))
-                or (len(input3_shape) != len(output_shape))
-            ):
-                error_result = True
+
+            for output in kwargs["result_tensors"]:
+                output_shape = output.shape
+                if (
+                    (len(input1_shape) != len(output_shape))
+                    or (len(input2_shape) != len(output_shape))
+                    or (len(input3_shape) != len(output_shape))
+                ):
+                    error_result = True
 
         info_dict = {
             "error_name": error_name,
@@ -1074,16 +1091,18 @@ class TosaErrorValidator:
             input3_shape = (
                 kwargs["input3"].shape if "input3" in kwargs else input2_shape
             )
-            output_shape = kwargs["result_tensor"].shape
-            for i in range(
-                min(len(input1_shape), len(input2_shape), len(input3_shape))
-            ):
-                if (
-                    (input1_shape[i] != 1 and input1_shape[i] != output_shape[i])
-                    or (input2_shape[i] != 1 and input2_shape[i] != output_shape[i])
-                    or (input3_shape[i] != 1 and input3_shape[i] != output_shape[i])
+
+            for output in kwargs["result_tensors"]:
+                output_shape = output.shape
+                for i in range(
+                    min(len(input1_shape), len(input2_shape), len(input3_shape))
                 ):
-                    error_result = True
+                    if (
+                        (input1_shape[i] != 1 and input1_shape[i] != output_shape[i])
+                        or (input2_shape[i] != 1 and input2_shape[i] != output_shape[i])
+                        or (input3_shape[i] != 1 and input3_shape[i] != output_shape[i])
+                    ):
+                        error_result = True
 
         info_dict = {
             "error_name": error_name,
@@ -2392,6 +2411,30 @@ class TosaErrorValidator:
         }
         return info_dict
 
+    @staticmethod
+    def evKernelNotPowerOfTwo(check=False, **kwargs):
+        error_name = ErrorIf.KernelNotPowerOfTwo
+        param_reqs = {"rank": None, "dtype": None, "shape": None}
+        error_result = False
+        error_reason = "kernel height and/or width not a power of two"
+
+        def is_power_of_two(x):
+            return math.log(x, 2).is_integer()
+
+        if check:
+            shape = kwargs["input_shape"]
+            if len(shape) == 3:
+                valid_kernel = is_power_of_two(shape[1]) and is_power_of_two(shape[2])
+                error_result = not valid_kernel
+
+        info_dict = {
+            "error_name": error_name,
+            "error_result": error_result,
+            "error_reason": error_reason,
+            "param_reqs": param_reqs,
+        }
+        return info_dict
+
 
 class TosaInvalidValidator:
     @staticmethod