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# Copyright (c) 2020-2022, ARM Limited.
# SPDX-License-Identifier: Apache-2.0
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 TGen:
"""A collection of functions to build tensor value arguments for an operator"""
def __init__(self):
pass
@staticmethod
def getRand(shape, dtype, rng):
if dtype == tf.float32:
return np.float32(
(rng.random(size=shape) - RAND_SHIFT_FACTOR) * RAND_SCALE_FACTOR
)
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))
raise Exception("Unsupported type: {}".format(dtype))
@staticmethod
def tgBasic(op, shape, dtype, rng):
# 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))
)
for i in range(const):
tf_consts.append(("const_{}".format(i), TGen.getRand(shape, dtype, rng)))
return tf_placeholders, tf_consts
@staticmethod
def tgBFuzz(op, shape, dtype, rng):
# 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
fuzz_arg = rng.integers(0, pl + const)
fuzz_idx = rng.integers(0, len(shape))
tf_placeholders = []
tf_consts = []
for i in range(pl):
if 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 tgConv2d(op, ifm_shape, dtype, rng):
# Take the shape and generate an input and filter
tf_placeholders = []
tf_consts = []
# 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.
filter_shape = (filter_h, filter_w, ifm_shape[3], ifm_shape[3] * 2)
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 = (ifm_shape[3] * 2,)
tf_consts.append(("const_1", TGen.getRand(bias_shape, dtype, rng)))
return tf_placeholders, tf_consts
@staticmethod
def tgDepthwiseConv2d(op, ifm_shape, dtype, rng):
# Take the shape and generate an input and filter
tf_placeholders = []
tf_consts = []
# 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)
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 = (ifm_shape[3] * 2,)
tf_consts.append(("const_1", TGen.getRand(bias_shape, dtype, rng)))
return tf_placeholders, tf_consts
@staticmethod
def tgTransposeConv2d(op, ifm_shape, dtype, rng):
# Take the shape and generate an input and filter
tf_placeholders = []
tf_consts = []
# 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.
filter_shape = (filter_h, filter_w, ifm_shape[3] * 2, ifm_shape[3])
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 = ifm_shape[3] * 2
tf_consts.append(("const_1", TGen.getRand(bias_shape, dtype, rng)))
return tf_placeholders, tf_consts
@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
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