diff options
Diffstat (limited to 'verif')
-rw-r--r-- | verif/generator/datagenerator.py | 2 | ||||
-rw-r--r-- | verif/generator/tosa_arg_gen.py | 79 | ||||
-rw-r--r-- | verif/generator/tosa_test_gen.py | 30 | ||||
-rw-r--r-- | verif/generator/tosa_utils.py | 5 |
4 files changed, 71 insertions, 45 deletions
diff --git a/verif/generator/datagenerator.py b/verif/generator/datagenerator.py index b5ef35d..743475c 100644 --- a/verif/generator/datagenerator.py +++ b/verif/generator/datagenerator.py @@ -78,7 +78,7 @@ class GenerateLibrary: size_bytes = size * 2 # Create buffer of bytes and initialize to zero buffer = (ct.c_ubyte * size_bytes)(0) - elif dtype == "INT32": + elif dtype == "INT32" or dtype == "SHAPE": # Create buffer and initialize to zero buffer = (ct.c_int32 * size)(0) size_bytes = size * 4 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 diff --git a/verif/generator/tosa_test_gen.py b/verif/generator/tosa_test_gen.py index 67ac367..159ee83 100644 --- a/verif/generator/tosa_test_gen.py +++ b/verif/generator/tosa_test_gen.py @@ -1558,22 +1558,14 @@ class TosaTestGen: ): assert len(inputs) == 2 a = inputs[0] - # second input is not properly generated yet - # new_shape = inputs[1] - - # modify inputs[1] by a shape tensor from new_shape arg value - new_shape_attr = args_dict["new_shape"] - shape_array = np.array(new_shape_attr) - shape = shape_array.shape - new_shape = self.ser.addPlaceholder(shape, DType.SHAPE, shape_array) - inputs[1] = new_shape - + shape = inputs[1] + shape_attr = args_dict["new_shape"] result_tensor = OutputShaper.reshapeOp( - self.ser, self.rng, a, new_shape_attr, error_name + self.ser, self.rng, a, shape_attr, error_name ) # Invalidate Input/Output list for error if checks. - input_list = [a.name, new_shape.name] + input_list = [a.name, shape.name] output_list = [result_tensor.name] pCount, cCount = op["operands"] num_operands = pCount + cCount @@ -1725,16 +1717,8 @@ class TosaTestGen: ): assert len(inputs) == 2 a = inputs[0] - # second input is not properly generated yet - # multiples = inputs[1] - - # modify inputs[1] by a shape tensor from multiples arg value + multiples = inputs[1] multiples_attr = args_dict["multiples"] - shape_array = np.int64(np.array(multiples_attr)) - shape = shape_array.shape - multiples = self.ser.addPlaceholder(shape, DType.SHAPE, shape_array) - inputs[1] = multiples - result_tensor = OutputShaper.tileOp( self.ser, self.rng, a, multiples_attr, error_name ) @@ -4236,7 +4220,7 @@ class TosaTestGen: "build_fcn": ( build_reshape, TosaTensorGen.tgBasic, - TosaTensorValuesGen.tvgLazyGenDefault, + TosaTensorValuesGen.tvgReshape, TosaArgGen.agReshape, ), "types": TYPE_FIB, @@ -4302,7 +4286,7 @@ class TosaTestGen: "build_fcn": ( build_tile, TosaTensorGen.tgBasic, - TosaTensorValuesGen.tvgLazyGenDefault, + TosaTensorValuesGen.tvgTile, TosaArgGen.agTile, ), "types": TYPE_FIB, diff --git a/verif/generator/tosa_utils.py b/verif/generator/tosa_utils.py index 3d733f4..33db95f 100644 --- a/verif/generator/tosa_utils.py +++ b/verif/generator/tosa_utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021-2023, ARM Limited. +# Copyright (c) 2021-2024, ARM Limited. # SPDX-License-Identifier: Apache-2.0 import struct import sys @@ -49,13 +49,14 @@ class DataGenType(IntEnum): OP_BOUNDARY = 2 OP_FULLSET = 3 OP_SPECIAL = 4 + FIXED_DATA = 5 def dtypeIsSupportedByCompliance(dtype): """Types supported by the new data generation and compliance flow.""" if isinstance(dtype, list) or isinstance(dtype, tuple): dtype = dtype[0] - return dtype in (DType.FP32, DType.FP16) + return dtype in (DType.FP32, DType.FP16, DType.SHAPE) def getOpNameFromOpListName(opName): |