Add support for FP8 to reference model

Signed-off-by: Won Jeon <won.jeon@arm.com>
Change-Id: I99b70f94aff2ccd4af64875697e124eb60bc5b08

6 files changed, 702 insertions, 28 deletions

diff --git a/verif/checker/tosa_result_checker.py b/verif/checker/tosa_result_checker.py
index 212c809..4d6d345 100644
--- a/verif/checker/tosa_result_checker.py
+++ b/verif/checker/tosa_result_checker.py
@@ -13,6 +13,7 @@ from checker.color_print import print_color
 from checker.verifier import VerifierError
 from checker.verifier import VerifierLibrary
 from generator.tosa_utils import float32_is_valid_bfloat16
+from generator.tosa_utils import float32_is_valid_float8
 from schemavalidation.schemavalidation import TestDescSchemaValidator
 
 
@@ -195,6 +196,18 @@ def test_check(
                 "reference_result: {ref_res_is_bf16}; test_result: {test_res_is_bf16}"
             )
             return (TestResult.INCORRECT_FORMAT, 0.0, msg)
+    if "fp8e4m3" in misc_checks or "fp8e5m2" in misc_checks:
+        # Ensure floats are valid float8 values
+        test_res_is_fp8 = all([float32_is_valid_float8(f) for f in test_result.flat])
+        ref_res_is_fp8 = all(
+            [float32_is_valid_float8(f) for f in reference_result.flat]
+        )
+        if not (test_res_is_fp8 and ref_res_is_fp8):
+            msg = (
+                "All output values must be valid float8. "
+                "reference_result: {ref_res_is_float8}; test_result: {test_res_is_float8}"
+            )
+            return (TestResult.INCORRECT_FLOAT, 0.0, msg)
 
     # for quantized test, allow +-(quantize_tolerance) error
     if reference_result.dtype in (
diff --git a/verif/conformance/tosa_main_profile_ops_info.json b/verif/conformance/tosa_main_profile_ops_info.json
index 7792417..7559c62 100644
--- a/verif/conformance/tosa_main_profile_ops_info.json
+++ b/verif/conformance/tosa_main_profile_ops_info.json
@@ -185,6 +185,30 @@
                         "2"
                     ]
                 ]
+            },
+            "float8": {
+                "from_version" : "v0.100.0",
+                "no_negative_tests": "true",
+                "generator_args": [
+                    [
+                        "--target-dtype",
+                        "fp8e4m3",
+                        "--target-dtype",
+                        "fp8e5m2",
+                        "--fp-values-range",
+                        "-max,max",
+                        "--tensor-dim-range",
+                        "32,64",
+                        "--target-rank",
+                        "1",
+                        "--target-rank",
+                        "2",
+                        "--target-rank",
+                        "3",
+                        "--num-rand-permutations",
+                        "2"
+                    ]
+                ]
             }
         },
         "selection": {
@@ -233,6 +257,24 @@
                         "--allow-pooling-and-conv-oversizes"
                     ]
                 ]
+            },
+            "float8": {
+                "from_version" : "v0.100.0",
+                "no_negative_tests": "true",
+                "generator_args": [
+                    [
+                        "--target-dtype",
+                        "fp8e4m3",
+                        "--target-dtype",
+                        "fp8e5m2",
+                        "--fp-values-range",
+                        "-max,max",
+                        "--tensor-dim-range",
+                        "32,64",
+                        "--num-rand-permutations",
+                        "2"
+                    ]
+                ]
             }
         },
         "selection": {
@@ -315,6 +357,30 @@
                         "2,65538,1,1"
                     ]
                 ]
+            },
+            "float8": {
+                "from_version" : "v0.100.0",
+                "no_negative_tests": "true",
+                "generator_args": [
+                    [
+                        "--target-dtype",
+                        "fp8e4m3",
+                        "--target-dtype",
+                        "fp8e5m2",
+                        "--fp-values-range",
+                        "-max,max",
+                        "--tensor-dim-range",
+                        "32,64",
+                        "--target-rank",
+                        "1",
+                        "--target-rank",
+                        "2",
+                        "--target-rank",
+                        "3",
+                        "--num-rand-permutations",
+                        "2"
+                    ]
+                ]
             }
         },
         "selection": {
@@ -527,6 +593,30 @@
                         "2"
                     ]
                 ]
+            },
+            "float8": {
+                "from_version" : "v0.100.0",
+                "no_negative_tests": "true",
+                "generator_args": [
+                    [
+                        "--target-dtype",
+                        "fp8e4m3",
+                        "--target-dtype",
+                        "fp8e5m2",
+                        "--fp-values-range",
+                        "-max,max",
+                        "--tensor-dim-range",
+                        "32,64",
+                        "--target-rank",
+                        "1",
+                        "--target-rank",
+                        "2",
+                        "--target-rank",
+                        "3",
+                        "--num-rand-permutations",
+                        "2"
+                    ]
+                ]
             }
         },
         "selection": {
@@ -592,6 +682,30 @@
                         "1,2,1,65529"
                     ]
                 ]
+            },
+            "float8": {
+                "from_version" : "v0.100.0",
+                "no_negative_tests": "true",
+                "generator_args": [
+                    [
+                        "--target-dtype",
+                        "fp8e4m3",
+                        "--target-dtype",
+                        "fp8e5m2",
+                        "--fp-values-range",
+                        "-max,max",
+                        "--tensor-dim-range",
+                        "32,64",
+                        "--target-rank",
+                        "1",
+                        "--target-rank",
+                        "2",
+                        "--target-rank",
+                        "3",
+                        "--num-rand-permutations",
+                        "2"
+                    ]
+                ]
             }
         },
         "selection": {
@@ -647,6 +761,24 @@
                         "--allow-pooling-and-conv-oversizes"
                     ]
                 ]
+            },
+            "float8": {
+                "from_version" : "v0.100.0",
+                "no_negative_tests": "true",
+                "generator_args": [
+                    [
+                        "--target-dtype",
+                        "fp8e4m3",
+                        "--target-dtype",
+                        "fp8e5m2",
+                        "--fp-values-range",
+                        "-max,max",
+                        "--tensor-dim-range",
+                        "32,64",
+                        "--num-rand-permutations",
+                        "2"
+                    ]
+                ]
             }
         },
         "selection": {
@@ -722,6 +854,28 @@
                         "--allow-pooling-and-conv-oversizes"
                     ]
                 ]
+            },
+            "float8": {
+                "from_version" : "v0.100.0",
+                "no_negative_tests": "true",
+                "generator_args": [
+                    [
+                        "--target-dtype",
+                        "fp8e4m3",
+                        "--target-dtype",
+                        "fp8e5m2",
+                        "--fp-values-range",
+                        "-max,max",
+                        "--target-shape",
+                        "1,7,18,5,4",
+                        "--target-shape",
+                        "1,6,12,17,3",
+                        "--tensor-dim-range",
+                        "1,4",
+                        "--num-rand-permutations",
+                        "2"
+                    ]
+                ]
             }
         },
         "selection": {
@@ -787,6 +941,24 @@
                         "--allow-pooling-and-conv-oversizes"
                     ]
                 ]
+            },
+            "float8": {
+                "from_version" : "v0.100.0",
+                "no_negative_tests": "true",
+                "generator_args": [
+                    [
+                        "--target-dtype",
+                        "fp8e4m3",
+                        "--target-dtype",
+                        "fp8e5m2",
+                        "--fp-values-range",
+                        "-max,max",
+                        "--tensor-dim-range",
+                        "32,64",
+                        "--num-rand-permutations",
+                        "2"
+                    ]
+                ]
             }
         },
         "selection": {
@@ -840,6 +1012,30 @@
                         "3"
                     ]
                 ]
+            },
+            "float8": {
+                "from_version" : "v0.100.0",
+                "no_negative_tests": "true",
+                "generator_args": [
+                    [
+                        "--target-dtype",
+                        "fp8e4m3",
+                        "--target-dtype",
+                        "fp8e5m2",
+                        "--fp-values-range",
+                        "-max,max",
+                        "--tensor-dim-range",
+                        "32,64",
+                        "--target-rank",
+                        "1",
+                        "--target-rank",
+                        "2",
+                        "--target-rank",
+                        "3",
+                        "--num-rand-permutations",
+                        "2"
+                    ]
+                ]
             }
         },
         "selection": {
@@ -1183,6 +1379,30 @@
                         "5000,1,1"
                     ]
                 ]
+            },
+            "float8": {
+                "from_version" : "v0.100.0",
+                "no_negative_tests": "true",
+                "generator_args": [
+                    [
+                        "--target-dtype",
+                        "fp8e4m3",
+                        "--target-dtype",
+                        "fp8e5m2",
+                        "--fp-values-range",
+                        "-max,max",
+                        "--tensor-dim-range",
+                        "32,64",
+                        "--target-rank",
+                        "1",
+                        "--target-rank",
+                        "2",
+                        "--target-rank",
+                        "3",
+                        "--num-rand-permutations",
+                        "2"
+                    ]
+                ]
             }
         },
         "selection": {
@@ -1505,6 +1725,30 @@
                         "1,65538,3"
                     ]
                 ]
+            },
+            "float8": {
+                "from_version" : "v0.100.0",
+                "no_negative_tests": "true",
+                "generator_args": [
+                    [
+                        "--target-dtype",
+                        "fp8e4m3",
+                        "--target-dtype",
+                        "fp8e5m2",
+                        "--fp-values-range",
+                        "-max,max",
+                        "--tensor-dim-range",
+                        "32,64",
+                        "--target-rank",
+                        "1",
+                        "--target-rank",
+                        "2",
+                        "--target-rank",
+                        "3",
+                        "--num-rand-permutations",
+                        "2"
+                    ]
+                ]
             }
         },
         "selection": {
@@ -1551,6 +1795,24 @@
                         "--allow-pooling-and-conv-oversizes"
                     ]
                 ]
+            },
+            "float8": {
+                "from_version" : "v0.100.0",
+                "no_negative_tests": "true",
+                "generator_args": [
+                    [
+                        "--target-dtype",
+                        "fp8e4m3",
+                        "--target-dtype",
+                        "fp8e5m2",
+                        "--fp-values-range",
+                        "-max,max",
+                        "--tensor-dim-range",
+                        "32,64",
+                        "--num-rand-permutations",
+                        "2"
+                    ]
+                ]
             }
         },
         "selection": {
@@ -1699,6 +1961,30 @@
                         "1,1,65539,1"
                     ]
                 ]
+            },
+            "float8": {
+                "from_version" : "v0.100.0",
+                "no_negative_tests": "true",
+                "generator_args": [
+                    [
+                        "--target-dtype",
+                        "fp8e4m3",
+                        "--target-dtype",
+                        "fp8e5m2",
+                        "--fp-values-range",
+                        "-max,max",
+                        "--tensor-dim-range",
+                        "32,64",
+                        "--target-rank",
+                        "1",
+                        "--target-rank",
+                        "2",
+                        "--target-rank",
+                        "3",
+                        "--num-rand-permutations",
+                        "2"
+                    ]
+                ]
             }
         },
         "selection": {
@@ -1889,6 +2175,30 @@
                         "2"
                     ]
                 ]
+            },
+            "float8": {
+                "from_version" : "v0.100.0",
+                "no_negative_tests": "true",
+                "generator_args": [
+                    [
+                        "--target-dtype",
+                        "fp8e4m3",
+                        "--target-dtype",
+                        "fp8e5m2",
+                        "--fp-values-range",
+                        "-max,max",
+                        "--tensor-dim-range",
+                        "32,64",
+                        "--target-rank",
+                        "1",
+                        "--target-rank",
+                        "2",
+                        "--target-rank",
+                        "3",
+                        "--num-rand-permutations",
+                        "2"
+                    ]
+                ]
             }
         },
         "selection": {
@@ -1935,6 +2245,30 @@
                         "1,65535,1,2"
                     ]
                 ]
+            },
+            "float8": {
+                "from_version" : "v0.100.0",
+                "no_negative_tests": "true",
+                "generator_args": [
+                    [
+                        "--target-dtype",
+                        "fp8e4m3",
+                        "--target-dtype",
+                        "fp8e5m2",
+                        "--fp-values-range",
+                        "-max,max",
+                        "--tensor-dim-range",
+                        "32,64",
+                        "--target-rank",
+                        "1",
+                        "--target-rank",
+                        "2",
+                        "--target-rank",
+                        "3",
+                        "--num-rand-permutations",
+                        "2"
+                    ]
+                ]
             }
         },
         "selection": {
@@ -2046,6 +2380,24 @@
                         "2989,6,1"
                     ]
                 ]
+            },
+            "float8": {
+                "from_version" : "v0.100.0",
+                "no_negative_tests": "true",
+                "generator_args": [
+                    [
+                        "--target-dtype",
+                        "fp8e4m3",
+                        "--target-dtype",
+                        "fp8e5m2",
+                        "--fp-values-range",
+                        "-max,max",
+                        "--tensor-dim-range",
+                        "32,64",
+                        "--num-rand-permutations",
+                        "2"
+                    ]
+                ]
             }
         },
         "selection": {
@@ -2091,6 +2443,30 @@
                         "1,65543,2,1"
                     ]
                 ]
+            },
+            "float8": {
+                "from_version" : "v0.100.0",
+                "no_negative_tests": "true",
+                "generator_args": [
+                    [
+                        "--target-dtype",
+                        "fp8e4m3",
+                        "--target-dtype",
+                        "fp8e5m2",
+                        "--fp-values-range",
+                        "-max,max",
+                        "--tensor-dim-range",
+                        "32,64",
+                        "--target-rank",
+                        "1",
+                        "--target-rank",
+                        "2",
+                        "--target-rank",
+                        "3",
+                        "--num-rand-permutations",
+                        "2"
+                    ]
+                ]
             }
         },
         "selection": {
@@ -2161,6 +2537,30 @@
                         "1"
                     ]
                 ]
+            },
+            "float8": {
+                "from_version" : "v0.100.0",
+                "no_negative_tests": "true",
+                "generator_args": [
+                    [
+                        "--target-dtype",
+                        "fp8e4m3",
+                        "--target-dtype",
+                        "fp8e5m2",
+                        "--fp-values-range",
+                        "-max,max",
+                        "--tensor-dim-range",
+                        "32,64",
+                        "--target-rank",
+                        "1",
+                        "--target-rank",
+                        "2",
+                        "--target-rank",
+                        "3",
+                        "--num-rand-permutations",
+                        "2"
+                    ]
+                ]
             }
         },
         "selection": {
@@ -2214,6 +2614,30 @@
                         "1"
                     ]
                 ]
+            },
+            "float8": {
+                "from_version" : "v0.100.0",
+                "no_negative_tests": "true",
+                "generator_args": [
+                    [
+                        "--target-dtype",
+                        "fp8e4m3",
+                        "--target-dtype",
+                        "fp8e5m2",
+                        "--fp-values-range",
+                        "-max,max",
+                        "--tensor-dim-range",
+                        "32,64",
+                        "--target-shape",
+                        "10,24,9,13",
+                        "--target-shape",
+                        "8,14,20,5",
+                        "--tensor-dim-range",
+                        "1,16",
+                        "--num-rand-permutations",
+                        "2"
+                    ]
+                ]
             }
         },
         "selection": {
@@ -3111,6 +3535,30 @@
                         "2"
                     ]
                 ]
+            },
+            "float8": {
+                "from_version" : "v0.100.0",
+                "no_negative_tests": "true",
+                "generator_args": [
+                    [
+                        "--target-dtype",
+                        "fp8e4m3",
+                        "--target-dtype",
+                        "fp8e5m2",
+                        "--fp-values-range",
+                        "-max,max",
+                        "--tensor-dim-range",
+                        "32,64",
+                        "--target-rank",
+                        "1",
+                        "--target-rank",
+                        "2",
+                        "--target-rank",
+                        "3",
+                        "--num-rand-permutations",
+                        "2"
+                    ]
+                ]
             }
         },
         "selection": {
diff --git a/verif/generator/tosa_arg_gen.py b/verif/generator/tosa_arg_gen.py
index 7ec0cfe..d0b9eb9 100644
--- a/verif/generator/tosa_arg_gen.py
+++ b/verif/generator/tosa_arg_gen.py
@@ -641,6 +641,8 @@ class TosaTensorValuesGen:
         DType.FP32: (1 << 128) - (1 << (127 - 23)),
         DType.FP16: (1 << 16) - (1 << (15 - 10)),
         DType.BF16: (1 << 128) - (1 << (127 - 7)),
+        DType.FP8E4M3: 448,
+        DType.FP8E5M2: 57344,
     }
 
     # Default lowest normal values for random numbers
@@ -648,6 +650,8 @@ class TosaTensorValuesGen:
         DType.FP32: np.exp2(-126),
         DType.FP16: np.exp2(-14),
         DType.BF16: np.exp2(-126),
+        DType.FP8E4M3: np.exp2(-9),
+        DType.FP8E5M2: np.exp2(-16),
     }
 
     @staticmethod
@@ -715,6 +719,8 @@ class TosaTensorValuesGen:
                     DType.FP16,
                     DType.FP32,
                     DType.BF16,
+                    DType.FP8E4M3,
+                    DType.FP8E5M2,
                 ):
                     # Change from inclusive to exclusive range
                     data_range = (data_range[0], data_range[1] + 1)
@@ -1734,7 +1740,13 @@ class TosaArgGen:
             and "data_gen" in testGen.TOSA_OP_LIST[opName]
             and gtu.dtypeIsSupportedByCompliance(dtype)
         ):
-            if dtype in [DType.FP16, DType.FP32, DType.BF16]:
+            if dtype in [
+                DType.FP16,
+                DType.FP32,
+                DType.BF16,
+                DType.FP8E4M3,
+                DType.FP8E5M2,
+            ]:
                 dataGenTypesList = testGen.TOSA_OP_LIST[opName]["data_gen"]["fp"]
             else:
                 dataGenTypesList = testGen.TOSA_OP_LIST[opName]["data_gen"]["int"]
@@ -2140,6 +2152,8 @@ class TosaArgGen:
             accum_dtypes = [DType.FP32]
         elif dtype == DType.FP32:
             accum_dtypes = [DType.FP32]
+        elif dtype == DType.FP8E4M3 or dtype == DType.FP8E5M2:
+            accum_dtypes = [DType.FP16]
         elif error_name is None:
             assert False, f"Invalid I/O DType for MatMul: {DTypeNames[dtype]}"
 
@@ -2350,7 +2364,13 @@ class TosaArgGen:
         if dtype in [DType.BOOL, DType.INT8, DType.INT16, DType.INT32]:
             pad_const_int = testGen.getRandNumberDType(dtype)
             pad_const_fp = 0
-        elif dtype in (DType.FP16, DType.BF16, DType.FP32):
+        elif dtype in (
+            DType.FP16,
+            DType.BF16,
+            DType.FP32,
+            DType.FP8E4M3,
+            DType.FP8E5M2,
+        ):
             pad_const_int = 0
             pad_const_fp = testGen.getRandNumberDType(dtype)
         else:
@@ -2468,6 +2488,8 @@ class TosaArgGen:
             accum_dtypes = [DType.FP16, DType.FP32]
         elif dtype == DType.BF16 or dtype == DType.FP32:
             accum_dtypes = [DType.FP32]
+        elif dtype == DType.FP8E4M3 or dtype == DType.FP8E5M2:
+            accum_dtypes = [DType.FP16]
         elif error_name is None:
             assert False, f"Invalid I/O DType for pooling: {DTypeNames[dtype]}"
         else:
@@ -2646,11 +2668,35 @@ class TosaArgGen:
         elif inDtype == DType.BOOL:
             dtypeList = [DType.INT8, DType.INT16, DType.INT32]
         elif inDtype == DType.FP16:
-            dtypeList = [DType.INT8, DType.INT16, DType.INT32, DType.FP32]
+            dtypeList = [
+                DType.INT8,
+                DType.INT16,
+                DType.INT32,
+                DType.FP32,
+                DType.FP8E4M3,
+                DType.FP8E5M2,
+            ]
         elif inDtype == DType.BF16:
-            dtypeList = [DType.INT8, DType.INT16, DType.INT32, DType.FP32]
+            dtypeList = [
+                DType.INT8,
+                DType.INT16,
+                DType.INT32,
+                DType.FP32,
+                DType.FP8E4M3,
+                DType.FP8E5M2,
+            ]
         elif inDtype == DType.FP32:
-            dtypeList = [DType.INT8, DType.INT16, DType.INT32, DType.FP16, DType.BF16]
+            dtypeList = [
+                DType.INT8,
+                DType.INT16,
+                DType.INT32,
+                DType.FP16,
+                DType.BF16,
+                DType.FP8E4M3,
+                DType.FP8E5M2,
+            ]
+        elif inDtype in [DType.FP8E4M3, DType.FP8E5M2]:
+            dtypeList = [DType.FP16, DType.BF16, DType.FP32]
         elif error_name == ErrorIf.WrongInputType:
             # Pick some potentially correct output type for incorrect input type
             dtypeList = [DType.BOOL, DType.INT8, DType.INT16, DType.FP32]
@@ -3232,6 +3278,10 @@ class TosaArgGen:
                 outputDTypeList = [DType.BF16]
             elif dtype == DType.FP32:
                 outputDTypeList = [DType.FP32]
+            elif dtype == DType.FP8E4M3:
+                outputDTypeList = [DType.FP8E4M3]
+            elif dtype == DType.FP8E5M2:
+                outputDTypeList = [DType.FP8E5M2]
             elif error_name == ErrorIf.WrongInputType:
                 # If an incorrect input type is used then we set a 'correct'
                 # output type to avoid other errors
diff --git a/verif/generator/tosa_error_if.py b/verif/generator/tosa_error_if.py
index 9a88acb..7a4d0d6 100644
--- a/verif/generator/tosa_error_if.py
+++ b/verif/generator/tosa_error_if.py
@@ -325,12 +325,32 @@ class TosaErrorIfArgGen:
 
     @staticmethod
     def eiCastErrorIf(testGen, input_dtype):
-        if input_dtype in [DType.BOOL, DType.FP32]:
+        # if input_dtype in [DType.BOOL, DType.FP32]:
+        #    outputDType = [DType.BOOL, DType.INT48, DType.FP32]
+        if input_dtype in [DType.BOOL]:
+            outputDType = [
+                DType.BOOL,
+                DType.INT48,
+                DType.FP32,
+                DType.FP16,
+                DType.BF16,
+                DType.FP8E4M3,
+                DType.FP8E5M2,
+            ]
+        elif input_dtype in [DType.FP32]:
             outputDType = [DType.BOOL, DType.INT48, DType.FP32]
         elif input_dtype in [DType.FP16, DType.BF16]:
             outputDType = [DType.BOOL, DType.INT48]
         elif input_dtype in [DType.INT8, DType.INT16, DType.INT32]:
             outputDType = [DType.INT48]
+        elif input_dtype in [DType.FP8E4M3, DType.FP8E5M2]:
+            outputDType = [
+                DType.BOOL,
+                DType.INT8,
+                DType.INT16,
+                DType.INT32,
+                DType.INT48,
+            ]
         else:
             assert False, f"input_dtype ({input_dtype}) not supported"
         return outputDType
@@ -476,13 +496,23 @@ class TosaErrorValidator:
                     )
                     or (input_dtype == DType.BF16 and output_dtype != DType.FP32)
                     or (input_dtype == DType.FP32 and output_dtype != DType.FP32)
+                    or (input_dtype == DType.FP8E4M3 and output_dtype != DType.FP16)
+                    or (input_dtype == DType.FP8E5M2 and output_dtype != DType.FP16)
                 ):
                     error_result = True
 
             elif op["op"] == Op.ARGMAX:
                 if (
                     input_dtype
-                    in [DType.INT8, DType.INT16, DType.FP16, DType.BF16, DType.FP32]
+                    in [
+                        DType.INT8,
+                        DType.INT16,
+                        DType.FP16,
+                        DType.BF16,
+                        DType.FP32,
+                        DType.FP8E4M3,
+                        DType.FP8E5M2,
+                    ]
                     and output_dtype != DType.INT32
                 ):
                     error_result = True
@@ -555,12 +585,26 @@ class TosaErrorValidator:
                     or (
                         input_dtype == DType.FP16
                         and output_dtype
-                        not in [DType.INT8, DType.INT16, DType.INT32, DType.FP32]
+                        not in [
+                            DType.INT8,
+                            DType.INT16,
+                            DType.INT32,
+                            DType.FP32,
+                            DType.FP8E4M3,
+                            DType.FP8E5M2,
+                        ]
                     )
                     or (
                         input_dtype == DType.BF16
                         and output_dtype
-                        not in [DType.INT8, DType.INT16, DType.INT32, DType.FP32]
+                        not in [
+                            DType.INT8,
+                            DType.INT16,
+                            DType.INT32,
+                            DType.FP32,
+                            DType.FP8E4M3,
+                            DType.FP8E5M2,
+                        ]
                     )
                     or (
                         input_dtype == DType.FP32
@@ -571,6 +615,17 @@ class TosaErrorValidator:
                             DType.INT32,
                             DType.FP16,
                             DType.BF16,
+                            DType.FP8E4M3,
+                            DType.FP8E5M2,
+                        ]
+                    )
+                    or (
+                        input_dtype in [DType.FP8E4M3, DType.FP8E5M2]
+                        and output_dtype
+                        not in [
+                            DType.FP16,
+                            DType.BF16,
+                            DType.FP32,
                         ]
                     )
                 ):
@@ -597,6 +652,10 @@ class TosaErrorValidator:
                     and output_dtype != DType.FP32
                     or input_dtype == DType.FP32
                     and output_dtype != DType.FP32
+                    or input_dtype == DType.FP8E4M3
+                    and output_dtype != DType.FP16
+                    or input_dtype == DType.FP8E5M2
+                    and output_dtype != DType.FP16
                 ):
                     error_result = True
                 # invalid input types are ignored, to avoid reporting multiple errors
@@ -2615,6 +2674,11 @@ class TosaErrorValidator:
                     DType.FP32,
                 ):
                     error_result = True
+                elif (
+                    input_dtype in (DType.FP8E4M3, DType.FP8E5M2)
+                    and accum_dtype != DType.FP16
+                ):
+                    error_result = True
 
         info_dict = {
             "error_name": error_name,
diff --git a/verif/generator/tosa_test_gen.py b/verif/generator/tosa_test_gen.py
index 4ead982..bc931dc 100644
--- a/verif/generator/tosa_test_gen.py
+++ b/verif/generator/tosa_test_gen.py
@@ -76,7 +76,7 @@ class TosaTestGen:
             return tuple(sorted(vals))
 
         self.random_float_range = {}
-        for dtype in (DType.FP32, DType.FP16, DType.BF16):
+        for dtype in (DType.FP32, DType.FP16, DType.BF16, DType.FP8E4M3, DType.FP8E5M2):
             self.random_float_range[dtype] = convertFPRange(
                 args.tensor_fp_value_range,
                 TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE[dtype],
@@ -152,7 +152,7 @@ class TosaTestGen:
         # Returns dtype value range boundaries (low, high)
         # The high boundary is excluded in the range
         # unless high_inclusive is True
-        if dtype in (DType.FP32, DType.FP16, DType.BF16):
+        if dtype in (DType.FP32, DType.FP16, DType.BF16, DType.FP8E4M3, DType.FP8E5M2):
             return self.random_float_range[dtype]
         elif dtype == DType.BOOL:
             rng = (0, 2)
@@ -197,7 +197,13 @@ class TosaTestGen:
             return np.uint8(self.rng.integers(low=low, high=high, size=shape))
         elif dtype in (DType.INT48, DType.SHAPE):
             return np.int64(self.rng.integers(low=low, high=high, size=shape))
-        elif dtype in (DType.FP16, DType.BF16, DType.FP32):
+        elif dtype in (
+            DType.FP16,
+            DType.BF16,
+            DType.FP32,
+            DType.FP8E4M3,
+            DType.FP8E5M2,
+        ):
             f_tensor = self.rng.uniform(low=low, high=high, size=shape)
 
             if dtype == DType.FP16:
@@ -207,6 +213,10 @@ class TosaTestGen:
                 if dtype == DType.BF16:
                     # Floor the last 16 bits of each f32 value
                     return np.float32(gtu.vect_f32_to_bf16(f32_tensor))
+                elif dtype == DType.FP8E4M3:
+                    return np.float32(gtu.vect_f32_to_fp8e4m3(f32_tensor))
+                elif dtype == DType.FP8E5M2:
+                    return np.float32(gtu.vect_f32_to_fp8e5m2(f32_tensor))
                 else:
                     return f32_tensor
         else:
@@ -266,6 +276,12 @@ class TosaTestGen:
         elif dtype == DType.BF16:
             rand_f32 = np.float32(self.rng.uniform(low=low, high=high))
             return gtu.vect_f32_to_bf16(rand_f32)
+        elif dtype == DType.FP8E4M3:
+            rand_f32 = np.float32(self.rng.uniform(low=low, high=high))
+            return gtu.vect_f32_to_fp8e4m3(rand_f32)
+        elif dtype == DType.FP8E5M2:
+            rand_f32 = np.float32(self.rng.uniform(low=low, high=high))
+            return gtu.vect_f32_to_fp8e5m2(rand_f32)
         elif dtype == DType.BOOL:
             return self.rng.choice([False, True])
         elif dtype == DType.INT48 or dtype == DType.SHAPE:
@@ -1408,8 +1424,11 @@ class TosaTestGen:
                 max_val = max_val.astype(np.float32)
 
             attr.ClampAttribute(self.ser.builder, 0, 0, min_val, max_val)
-        else:
+        elif a.dtype in (DType.INT8, DType.INT16):
             attr.ClampAttribute(self.ser.builder, min_val, max_val, 0, 0)
+        else:
+            # to avoid internal error for incorrect input types
+            attr.ClampAttribute(self.ser.builder, 0, 0, 0, 0)
 
         self.ser.addOperator(op["op"], input_list, output_list, attr)
 
@@ -3190,7 +3209,13 @@ class TosaTestGen:
     ]
     TYPE_FI16 = [DType.FP32, DType.INT16]
 
-    TYPE_NARROW_INT_FP = [DType.INT8, DType.INT16, DType.FP16, DType.BF16, DType.FP32]
+    TYPE_NARROW_INT_FP = [
+        DType.INT8,
+        DType.INT16,
+        DType.FP16,
+        DType.BF16,
+        DType.FP32,
+    ]
 
     # List of [Input Type 1, Input Type 2, Accumulator Type]
     TYPE_CONV = [
@@ -3201,6 +3226,8 @@ class TosaTestGen:
         [DType.FP16, DType.FP16, DType.FP32],
         [DType.BF16, DType.BF16, DType.FP32],
         [DType.FP32, DType.FP32, DType.FP32],
+        [DType.FP8E4M3, DType.FP8E4M3, DType.FP16],
+        [DType.FP8E5M2, DType.FP8E5M2, DType.FP16],
     ]
 
     DEFAULT_RANK_RANGE = (1, TOSA_TENSOR_MAX_RANK)
@@ -3217,7 +3244,7 @@ class TosaTestGen:
                 TosaTensorValuesGen.tvgLazyGenDefault,
                 TosaArgGen.agAxis,
             ),
-            "types": TYPE_NARROW_INT_FP,
+            "types": TYPE_NARROW_INT_FP + [DType.FP8E4M3, DType.FP8E5M2],
             "error_if_validators": (
                 TosaErrorValidator.evAxisSmallerZero,
                 TosaErrorValidator.evAxisLargerRank,
@@ -3244,7 +3271,7 @@ class TosaTestGen:
                 TosaArgGen.agPooling,
             ),
             "qgen": TosaQuantGen.qgUnary,
-            "types": TYPE_NARROW_INT_FP,
+            "types": TYPE_NARROW_INT_FP + [DType.FP8E4M3, DType.FP8E5M2],
             "invalid_test_validators": (TosaInvalidValidator.ivHeightWidthInvalid,),
             "error_if_validators": (
                 TosaErrorValidator.evKernelSmallerOne,
@@ -3402,7 +3429,7 @@ class TosaTestGen:
                 TosaArgGen.agMatMul,
             ),
             "qgen": TosaQuantGen.qgMatmul,
-            "types": TYPE_NARROW_INT_FP,
+            "types": TYPE_NARROW_INT_FP + [DType.FP8E4M3, DType.FP8E5M2],
             "error_if_validators": (
                 TosaErrorValidator.evInputZeroPointNotZero,
                 TosaErrorValidator.evWrongRank,
@@ -3425,7 +3452,7 @@ class TosaTestGen:
                 TosaTensorValuesGen.tvgLazyGenDefault,
                 TosaArgGen.agPooling,
             ),
-            "types": TYPE_NARROW_INT_FP,
+            "types": TYPE_NARROW_INT_FP + [DType.FP8E4M3, DType.FP8E5M2],
             "invalid_test_validators": (TosaInvalidValidator.ivHeightWidthInvalid,),
             "error_if_validators": (
                 TosaErrorValidator.evKernelSmallerOne,
@@ -4389,7 +4416,7 @@ class TosaTestGen:
                 TosaTensorValuesGen.tvgConcat,
                 TosaArgGen.agAxis,
             ),
-            "types": TYPE_FIB,
+            "types": TYPE_FIB + [DType.FP8E4M3, DType.FP8E5M2],
             "error_if_validators": (
                 TosaErrorValidator.evAxisLargerRank,
                 TosaErrorValidator.evAxisSmallerZero,
@@ -4413,7 +4440,7 @@ class TosaTestGen:
                 TosaTensorValuesGen.tvgPad,
                 TosaArgGen.agPad,
             ),
-            "types": TYPE_FIB,
+            "types": TYPE_FIB + [DType.FP8E4M3, DType.FP8E5M2],
             "error_if_validators": (
                 TosaErrorValidator.evWrongInputType,
                 TosaErrorValidator.evPadSmallerZero,
@@ -4437,7 +4464,7 @@ class TosaTestGen:
                 TosaTensorValuesGen.tvgLazyGenDefault,
                 TosaArgGen.agAxis,
             ),
-            "types": TYPE_FIB,
+            "types": TYPE_FIB + [DType.FP8E4M3, DType.FP8E5M2],
             "error_if_validators": (
                 TosaErrorValidator.evAxisLargerRank,
                 TosaErrorValidator.evAxisSmallerZero,
@@ -4456,7 +4483,7 @@ class TosaTestGen:
                 TosaTensorValuesGen.tvgReshape,
                 TosaArgGen.agReshape,
             ),
-            "types": TYPE_FIB,
+            "types": TYPE_FIB + [DType.FP8E4M3, DType.FP8E5M2],
             "error_if_validators": (
                 TosaErrorValidator.evTensorSizeInputOutputMismatch,
                 TosaErrorValidator.evWrongInputType,
@@ -4477,7 +4504,7 @@ class TosaTestGen:
                 TosaTensorValuesGen.tvgLazyGenDefault,
                 TosaArgGen.agAxis,
             ),
-            "types": TYPE_FIB,
+            "types": TYPE_FIB + [DType.FP8E4M3, DType.FP8E5M2],
             "error_if_validators": (
                 TosaErrorValidator.evAxisSmallerZero,
                 TosaErrorValidator.evAxisLargerRank,
@@ -4500,7 +4527,7 @@ class TosaTestGen:
                 TosaTensorValuesGen.tvgSlice,
                 TosaArgGen.agSlice,
             ),
-            "types": TYPE_FIB,
+            "types": TYPE_FIB + [DType.FP8E4M3, DType.FP8E5M2],
             "error_if_validators": (
                 # TODO Turn off these error categories for now as the reference
                 # model cannot allocate memory space for empty tensor. We probably
@@ -4532,7 +4559,7 @@ class TosaTestGen:
                 TosaTensorValuesGen.tvgTile,
                 TosaArgGen.agTile,
             ),
-            "types": TYPE_FIB,
+            "types": TYPE_FIB + [DType.FP8E4M3, DType.FP8E5M2],
             "error_if_validators": (
                 TosaErrorValidator.evWrongInputType,
                 TosaErrorValidator.evWrongOutputType,
@@ -4555,7 +4582,7 @@ class TosaTestGen:
                 TosaTensorValuesGen.tvgLazyGenDefault,
                 TosaArgGen.agTranspose,
             ),
-            "types": TYPE_FIB,
+            "types": TYPE_FIB + [DType.FP8E4M3, DType.FP8E5M2],
             "error_if_validators": (
                 TosaErrorValidator.evIndexOutsideBounds,
                 TosaErrorValidator.evIndexUsedTwice,
@@ -4581,7 +4608,7 @@ class TosaTestGen:
                 TosaTensorValuesGen.tvgLazyGenDefault,
                 TosaArgGen.agNone,
             ),
-            "types": TYPE_FIB + [DType.INT48],
+            "types": TYPE_FIB + [DType.INT48, DType.FP8E4M3, DType.FP8E5M2],
             "data_gen": {
                 "fp": (gtu.DataGenType.PSEUDO_RANDOM,),
             },
@@ -4618,6 +4645,8 @@ class TosaTestGen:
                 DType.FP16,
                 DType.BF16,
                 DType.FP32,
+                DType.FP8E4M3,
+                DType.FP8E5M2,
             ),
             "error_if_validators": (
                 TosaErrorValidator.evWrongInputType,
@@ -4640,7 +4669,7 @@ class TosaTestGen:
                 TosaTensorValuesGen.tvgScatter,
                 TosaArgGen.agNone,
             ),
-            "types": TYPE_INT_FP,
+            "types": TYPE_INT_FP + [DType.FP8E4M3, DType.FP8E5M2],
             "error_if_validators": (
                 TosaErrorValidator.evWrongInputType,
                 TosaErrorValidator.evWrongOutputType,
@@ -4709,6 +4738,8 @@ class TosaTestGen:
                 DType.INT16,
                 DType.INT32,
                 DType.BOOL,
+                DType.FP8E4M3,
+                DType.FP8E5M2,
             ),
             "error_if_validators": (
                 TosaErrorValidator.evWrongInputType,
@@ -5141,6 +5172,8 @@ class OutputShaper:
                 DType.FP32,
                 DType.FP16,
                 DType.BF16,
+                DType.FP8E4M3,
+                DType.FP8E5M2,
             ]
             wrong_dtypes = list(set(all_dtypes) - set([DType.INT32]))
             outputDType = rng.choice(wrong_dtypes)
@@ -5194,6 +5227,8 @@ class OutputShaper:
         if error_name == ErrorIf.WrongOutputType:
             if ifm.dtype == DType.FP16:
                 excludes = [DType.FP16, DType.FP32]
+            if ifm.dtype in [DType.FP8E4M3, DType.FP8E5M2]:
+                excludes = [DType.FP16]
             else:
                 excludes = [out_dtype]
             wrong_dtypes = list(gtu.usableDTypes(excludes=excludes))
@@ -5344,6 +5379,8 @@ class OutputShaper:
                 DType.FP32,
                 DType.FP16,
                 DType.BF16,
+                DType.FP8E4M3,
+                DType.FP8E5M2,
             ]
             wrong_dtypes = list(set(all_dtypes) - set([ifm.dtype]))
             outputDType = rng.choice(wrong_dtypes)
@@ -5383,6 +5420,8 @@ class OutputShaper:
                     DType.FP32,
                     DType.FP16,
                     DType.BF16,
+                    DType.FP8E4M3,
+                    DType.FP8E5M2,
                 )
             elif a.dtype == DType.INT16:
                 incorrect_types = (
@@ -5393,6 +5432,20 @@ class OutputShaper:
                     DType.FP32,
                     DType.FP16,
                     DType.BF16,
+                    DType.FP8E4M3,
+                    DType.FP8E5M2,
+                )
+            elif a.dtype == DType.FP8E4M3 or a.dtype == DType.FP8E5M2:
+                incorrect_types = (
+                    DType.INT4,
+                    DType.INT8,
+                    DType.INT16,
+                    DType.INT32,
+                    DType.INT48,
+                    DType.FP32,
+                    DType.BF16,
+                    DType.FP8E4M3,
+                    DType.FP8E5M2,
                 )
             elif (
                 a.dtype == DType.FP32 or a.dtype == DType.FP16 or a.dtype == DType.BF16
@@ -5403,6 +5456,8 @@ class OutputShaper:
                     DType.INT16,
                     DType.INT32,
                     DType.INT48,
+                    DType.FP8E4M3,
+                    DType.FP8E5M2,
                 )
             out_dtype = rng.choice(a=incorrect_types)
         elif error_name == ErrorIf.WrongInputType:
@@ -5669,6 +5724,8 @@ class OutputShaper:
                 DType.FP32,
                 DType.FP16,
                 DType.BF16,
+                DType.FP8E4M3,
+                DType.FP8E5M2,
             ]
             wrong_dtypes = list(set(all_dtypes) - set([values_in.dtype]))
             outputDType = rng.choice(wrong_dtypes)
diff --git a/verif/generator/tosa_utils.py b/verif/generator/tosa_utils.py
index 76e7388..31a0ff0 100644
--- a/verif/generator/tosa_utils.py
+++ b/verif/generator/tosa_utils.py
@@ -27,6 +27,8 @@ DTYPE_ATTRIBUTES = {
     DType.FP16: {"str": "f16", "width": 16, "json": "FP16"},
     DType.BF16: {"str": "bf16", "width": 16, "json": "BF16"},
     DType.FP32: {"str": "f32", "width": 32, "json": "FP32"},
+    DType.FP8E4M3: {"str": "f8e4m3", "width": 8, "json": "FP8E4M3"},
+    DType.FP8E5M2: {"str": "f8e5m2", "width": 8, "json": "FP8E5M2"},
 }
 
 
@@ -186,6 +188,16 @@ def get_wrong_output_type(op_name, rng, input_dtype):
                 DType.INT32,
                 DType.INT48,
             )
+        elif input_dtype == DType.FP8E4M3 or input_dtype == DType.FP8E5M2:
+            incorrect_types = (
+                DType.INT4,
+                DType.INT8,
+                DType.INT16,
+                DType.INT32,
+                DType.INT48,
+                DType.FP32,
+                DType.BF16,
+            )
     else:
         # Assume all types but the input type are incorrect
         incorrect_types = list(usableDTypes(excludes=(input_dtype,)))
@@ -209,6 +221,12 @@ def float32_is_valid_bfloat16(f):
     return f32_bits[16:] == "0" * 16
 
 
+def float32_is_valid_float8(f):
+    """Return True if float value is valid float8."""
+    f32_bits = get_float32_bitstring(f)
+    return f32_bits[8:] == "0" * 24
+
+
 def get_float32_bitstring(f):
     """Return a big-endian string of bits representing a 32 bit float."""
     f32_bits_as_int = struct.unpack(">L", struct.pack(">f", f))[0]
@@ -232,6 +250,30 @@ def float32_to_bfloat16(f):
     return struct.unpack("@f", fp_bytes)[0]  # native byteorder
 
 
+def float32_to_fp8e4m3(f):
+    """Turns fp32 value into fp8e4m3"""
+    f32_bits = get_float32_bitstring(f)
+    fp8_bits = f32_bits[0] + f32_bits[1:5] + f32_bits[9:12] + "0" * 24
+    fp_bytes = int(fp8_bits, 2).to_bytes(4, byteorder=sys.byteorder)
+    return struct.unpack("@f", fp_bytes)[0]  # native byteorder
+
+
+def float32_to_fp8e5m2(f):
+    """Turns fp32 value into fp8e5m2"""
+    f32_bits = get_float32_bitstring(f)
+    fp8_bits = f32_bits[0] + f32_bits[1:6] + f32_bits[9:11] + "0" * 24
+    fp_bytes = int(fp8_bits, 2).to_bytes(4, byteorder=sys.byteorder)
+    return struct.unpack("@f", fp_bytes)[0]
+
+
 vect_f32_to_bf16 = np.vectorize(
     float32_to_bfloat16, otypes=(np.float32,)
 )  # NumPy vectorize: applies function to vector faster than looping
+
+vect_f32_to_fp8e4m3 = np.vectorize(
+    float32_to_fp8e4m3, otypes=(np.float32,)
+)  # NumPy vectorize: applies function to vector faster than looping
+
+vect_f32_to_fp8e5m2 = np.vectorize(
+    float32_to_fp8e5m2, otypes=(np.float32,)
+)  # Numpy vectorize: applies function to vector faster than looping