| # Copyright (c) 2020-2023, ARM Limited. |
| # SPDX-License-Identifier: Apache-2.0 |
| import enum |
| |
| import numpy as np |
| import tensorflow as tf |
| |
| # FIXME: replace hardcoded '* 2' with random integers, where possible |
| |
| # The scaling factor for random numbers generated in input tensors. The |
| # random numbers are calculated as: |
| # (np.random.rand() - RAND_SHIFT_FACTOR) * RAND_SCALE_FACTOR |
| # FIXME: improve range here |
| RAND_SCALE_FACTOR = 4.0 |
| # Amount to add to random numbers |
| RAND_SHIFT_FACTOR = 0.5 |
| |
| RAND_INT_MIN = -128 |
| RAND_INT_MAX = 128 |
| |
| |
| class ElemSignedness(enum.Enum): |
| ALL_RANGE = 1 |
| POSITIVE = 2 |
| NEGATIVE = 3 |
| |
| |
| class TGen: |
| """A collection of functions to build tensor value arguments for an operator""" |
| |
| def __init__(self): |
| pass |
| |
| @staticmethod |
| def getRand(shape, dtype, rng, elem_signedness=ElemSignedness.ALL_RANGE): |
| if elem_signedness == ElemSignedness.POSITIVE: |
| RAND_SHIFT_FACTOR = 0 |
| elif elem_signedness == ElemSignedness.NEGATIVE: |
| RAND_SHIFT_FACTOR = 1 |
| else: |
| RAND_SHIFT_FACTOR = 0.5 |
| |
| if dtype == tf.float32: |
| return ( |
| np.float32( |
| (rng.random(size=shape) - RAND_SHIFT_FACTOR) * RAND_SCALE_FACTOR |
| ) |
| if shape != () |
| else np.float32(rng.random()) |
| ) |
| if dtype == tf.float16: |
| return np.float16( |
| (rng.random(size=shape) - RAND_SHIFT_FACTOR) * RAND_SCALE_FACTOR |
| ) |
| if dtype == tf.int32: |
| return np.int32( |
| rng.integers(low=RAND_INT_MIN, high=RAND_INT_MAX, size=shape) |
| ) |
| if dtype == tf.uint32: |
| return np.uint32(rng.integers(low=0, high=RAND_INT_MAX, size=shape)) |
| if dtype == tf.bool: |
| return np.bool_(rng.choice(a=[False, True], size=shape)) |
| if dtype == tf.complex64: |
| return TGen.getRand(shape, np.float32, rng) + 1j * TGen.getRand( |
| shape, np.float32, rng |
| ) |
| |
| raise Exception("Unsupported type: {}".format(dtype)) |
| |
| @staticmethod |
| def tgBasicPositive(op, shape, dtype, rng, elem_signedness=ElemSignedness.POSITIVE): |
| return TGen.tgBasic(op, shape, dtype, rng, elem_signedness) |
| |
| @staticmethod |
| def tgBasic(op, shape, dtype, rng, elem_signedness=ElemSignedness.ALL_RANGE): |
| # Build random tensor placeholder node args of a given shape |
| pl, const = op["operands"] |
| |
| tf_placeholders = [] |
| tf_consts = [] |
| |
| for i in range(pl): |
| tf_placeholders.append( |
| ( |
| "placeholder_{}".format(i), |
| TGen.getRand(shape, dtype, rng, elem_signedness), |
| ) |
| ) |
| |
| for i in range(const): |
| tf_consts.append( |
| ("const_{}".format(i), TGen.getRand(shape, dtype, rng, elem_signedness)) |
| ) |
| |
| return tf_placeholders, tf_consts |
| |
| @staticmethod |
| def tgBFuzz(op, shape, dtype, rng, for_tflite_converter=True): |
| # Build random tensor placeholder node args of a given shape, optionally |
| # fuzzing the arguments with random 1's to force broadcasting |
| |
| pl, const = op["operands"] |
| |
| assert const == 0 |
| |
| if not for_tflite_converter: |
| fuzz_arg = rng.integers(0, pl + const) |
| fuzz_idx = rng.integers(0, len(shape)) |
| |
| tf_placeholders = [] |
| tf_consts = [] |
| |
| for i in range(pl): |
| if not for_tflite_converter and i == fuzz_arg: |
| # Insert the broadcast in one dimension index |
| s_fuzz = list(shape) |
| s_fuzz[fuzz_idx] = 1 |
| s_fuzz = tuple(s_fuzz) |
| i_shape = s_fuzz |
| else: |
| i_shape = shape |
| |
| tf_placeholders.append( |
| ("placeholder_{}".format(i), TGen.getRand(i_shape, dtype, rng)) |
| ) |
| |
| return tf_placeholders, tf_consts |
| |
| @staticmethod |
| def tgConvCommon(op, ifm_shape, filter_shape, out_channels, dtype, rng): |
| |
| # Take the shape and generate an input and filter |
| tf_placeholders = [] |
| tf_consts = [] |
| tf_placeholders.append(("placeholder_0", TGen.getRand(ifm_shape, dtype, rng))) |
| tf_consts.append(("const_0", TGen.getRand(filter_shape, dtype, rng))) |
| |
| try: |
| bias = op["bias"] |
| except KeyError: |
| bias = False |
| |
| if bias: |
| # bias is 1D and size == output channels |
| bias_shape = (out_channels,) |
| tf_consts.append(("const_1", TGen.getRand(bias_shape, dtype, rng))) |
| |
| return tf_placeholders, tf_consts |
| |
| @staticmethod |
| def tgConv2d(op, ifm_shape, dtype, rng): |
| |
| # Require rank 4 shape |
| if len(ifm_shape) != 4: |
| return [], [] |
| |
| filter_h, filter_w = op["filter"] |
| |
| # TODO: Hard-code the test by making the OFM depth 2x the IFM depth. |
| # Could randomize this in the future. |
| out_channels = ifm_shape[3] * 2 |
| filter_shape = (filter_h, filter_w, ifm_shape[3], out_channels) |
| |
| return TGen.tgConvCommon(op, ifm_shape, filter_shape, out_channels, dtype, rng) |
| |
| @staticmethod |
| def tgDepthwiseConv2d(op, ifm_shape, dtype, rng): |
| |
| # Require rank 4 shape |
| if len(ifm_shape) != 4: |
| return [], [] |
| |
| filter_h, filter_w = op["filter"] |
| |
| # TODO: Hard-code the test by making the channel_multiplier=2. |
| # Could randomize this in the future. |
| filter_shape = (filter_h, filter_w, ifm_shape[3], 2) |
| out_channels = ifm_shape[3] * 2 |
| |
| return TGen.tgConvCommon(op, ifm_shape, filter_shape, out_channels, dtype, rng) |
| |
| @staticmethod |
| def tgTransposeConv2d(op, ifm_shape, dtype, rng): |
| |
| # Require rank 4 shape |
| if len(ifm_shape) != 4: |
| return [], [] |
| |
| filter_h, filter_w = op["filter"] |
| |
| # TODO: Hard-code the test by making the IFM depth 2x the OFM depth. |
| # Could randomize this in the future. |
| out_channels = ifm_shape[3] * 2 |
| filter_shape = (filter_h, filter_w, out_channels, ifm_shape[3]) |
| |
| return TGen.tgConvCommon(op, ifm_shape, filter_shape, out_channels, dtype, rng) |
| |
| @staticmethod |
| def tgConv3d(op, ifm_shape, dtype, rng): |
| |
| # Require rank 5 shape |
| if len(ifm_shape) != 5: |
| return [], [] |
| |
| filter_d, filter_h, filter_w = op["filter"] |
| |
| # TODO: Hard-code the test by making the OFM depth 2x the IFM depth. |
| # Could randomize this in the future. |
| in_channels = ifm_shape[4] |
| out_channels = in_channels * 2 |
| filter_shape = (filter_d, filter_h, filter_w, in_channels, out_channels) |
| |
| return TGen.tgConvCommon(op, ifm_shape, filter_shape, out_channels, dtype, rng) |
| |
| @staticmethod |
| def tgPooling(op, shapes, dtype, rng): |
| # Pooling does nothing special except filter out non-rank-4 tensors |
| if len(shapes) != 4: |
| return [], [] |
| |
| return TGen.tgBasic(op, shapes, dtype, rng) |
| |
| @staticmethod |
| def tgMatmul(op, ifm_shape, dtype, rng): |
| # Take the shape and generate an input and filter |
| tf_placeholders = [] |
| tf_consts = [] |
| |
| if len(ifm_shape) < 2: |
| return [], [] |
| |
| # For ifm_shape = [..., N, K] |
| # Generate rhs tensor with shape [..., K x (2 * N)] |
| tf_placeholders.append(("placeholder_0", TGen.getRand(ifm_shape, dtype, rng))) |
| |
| shape_rhs = list(ifm_shape) |
| shape_rhs[-2] = ifm_shape[-1] |
| shape_rhs[-1] = ifm_shape[-2] * 2 |
| tf_placeholders.append( |
| ( |
| "placeholder_1", |
| TGen.getRand(shape_rhs, dtype, rng), |
| ) |
| ) |
| |
| return tf_placeholders, tf_consts |
| |
| @staticmethod |
| def tgOneHot(op, shape, dtype, rng): |
| # Build random tensor placeholder node args of a given shape |
| pl, const = op["operands"] |
| |
| assert pl == 3 and const == 1 |
| |
| tf_placeholders = [] |
| tf_consts = [] |
| |
| # depth |
| depth = np.int32(rng.integers(low=1, high=32, size=None)) |
| tf_consts.append(("const_0", depth)) |
| |
| # indices |
| indices = np.int32(rng.integers(low=0, high=depth, size=shape)) |
| tf_placeholders.append(("placeholder_0", indices)) |
| |
| # on_value |
| tf_placeholders.append(("placeholder_1", TGen.getRand(None, dtype, rng))) |
| |
| # off_value |
| tf_placeholders.append(("placeholder_2", TGen.getRand(None, dtype, rng))) |
| |
| return tf_placeholders, tf_consts |
| |
| @staticmethod |
| def tgSelect(op, shape, dtype, rng): |
| # Build random tensor placeholder node args of a given shape |
| pl, const = op["operands"] |
| assert pl == 3 and const == 0 |
| |
| tf_placeholders = [] |
| tf_consts = [] |
| |
| # selector |
| tf_placeholders.append(("placeholder_0", TGen.getRand(None, tf.bool, rng))) |
| # inputs |
| tf_placeholders.append(("placeholder_1", TGen.getRand(shape, dtype, rng))) |
| tf_placeholders.append(("placeholder_2", TGen.getRand(shape, dtype, rng))) |
| |
| return tf_placeholders, tf_consts |
| |
| @staticmethod |
| def tgRecurrent(op, ifm_shape, dtype, rng): |
| # Require rank 3 shape for recurrent networks |
| if len(ifm_shape) != 3: |
| return [], [] |
| pl, const = op["operands"] |
| |
| tf_placeholders = [] |
| tf_consts = [] |
| |
| for i in range(pl): |
| tf_placeholders.append( |
| ("placeholder_{}".format(i), TGen.getRand(ifm_shape, dtype, rng)) |
| ) |
| |
| for i in range(const): |
| tf_consts.append( |
| ("const_{}".format(i), TGen.getRand(ifm_shape, dtype, rng)) |
| ) |
| |
| return tf_placeholders, tf_consts |
| |
| @staticmethod |
| def tgRFFT2d(op, shape, dtype, rng): |
| # Require rank 3 shape |
| if len(shape) != 3: |
| return [], [] |
| |
| return TGen.tgBasic(op, shape, dtype, rng) |
| |
| @staticmethod |
| def tgComplexComponents(op, shape, dtype, rng): |
| # Temporarily require up to rank 3 shape, due to |
| # slice maximum rank limitiation. |
| if len(shape) > 3: |
| return [], [] |
| |
| return TGen.tgBasic(op, shape, dtype, rng) |
| |
| @staticmethod |
| def tgBroadcastTo(op, shape, dtype, rng): |
| |
| pl, const = op["operands"] |
| |
| assert pl == 1 |
| assert const == 1 |
| |
| tf_placeholders = [] |
| tf_consts = [] |
| |
| shape_list = list(shape) |
| t_shape_list = [] |
| s_shape_list = [] |
| for i in range(len(shape)): |
| dim = shape_list[i] |
| if rng.integers(0, 1) == 0: |
| # append dim in s_shape_list, and 1 in t_shape_list unless it is still empty |
| s_shape_list.append(dim) |
| if len(t_shape_list) > 0: |
| t_shape_list.append(1) |
| else: |
| # append 1 in s_shape_list, and dim in t_shape_list |
| s_shape_list.append(1) |
| t_shape_list.append(dim) |
| |
| # if t_shape_list is empty, then insert 1 |
| if len(t_shape_list) == 0: |
| t_shape_list.append(1) |
| |
| tf_placeholders.append( |
| ("placeholder_0", TGen.getRand(tuple(t_shape_list), dtype, rng)) |
| ) |
| |
| tf_consts.append(("shape", tuple(s_shape_list))) |
| |
| return tf_placeholders, tf_consts |