import numpy as np
import torch
from torch.utils.benchmark import Fuzzer, FuzzedParameter, ParameterAlias, FuzzedSparseTensor
_MIN_DIM_SIZE = 16
_MAX_DIM_SIZE = 16 * 1024 ** 2
_POW_TWO_SIZES = tuple(2 ** i for i in range(
int(np.log2(_MIN_DIM_SIZE)),
int(np.log2(_MAX_DIM_SIZE)) + 1,
))
class UnaryOpSparseFuzzer(Fuzzer):
def __init__(self, seed, dtype=torch.float32, cuda=False):
super().__init__(
parameters=[
# Sparse dim parameter of x. (e.g. 1D, 2D, or 3D.)
FuzzedParameter("dim_parameter", distribution={1: 0.3, 2: 0.4, 3: 0.3}, strict=True),
FuzzedParameter(
name="sparse_dim",
distribution={1: 0.4, 2: 0.4, 3: 0.2},
strict=True
),
# Shapes for `x`.
# It is important to test all shapes, however
# powers of two are especially important and therefore
# warrant special attention. This is done by generating
# both a value drawn from all integers between the min and
# max allowed values, and another from only the powers of two
# (both distributions are loguniform) and then randomly
# selecting between the two.
[
FuzzedParameter(
name=f"k_any_{i}",
minval=_MIN_DIM_SIZE,
maxval=_MAX_DIM_SIZE,
distribution="loguniform",
) for i in range(3)
],
[
FuzzedParameter(
name=f"k_pow2_{i}",
distribution={size: 1. / len(_POW_TWO_SIZES) for size in _POW_TWO_SIZES}
) for i in range(3)
],
[
FuzzedParameter(
name=f"k{i}",
distribution={
ParameterAlias(f"k_any_{i}"): 0.8,
ParameterAlias(f"k_pow2_{i}"): 0.2,
},
strict=True,
) for i in range(3)
],
FuzzedParameter(
name="density",
distribution={0.1: 0.4, 0.05: 0.3, 0.01: 0.3},
),
FuzzedParameter(
name="coalesced",
distribution={True: 0.5, False: 0.5},
),
FuzzedParameter(name="random_value", minval=0, maxval=2 ** 32 - 1, distribution="uniform"),
],
tensors=[
FuzzedSparseTensor(
name="x",
size=("k0", "k1", "k2"),
dim_parameter="dim_parameter",
sparse_dim="sparse_dim",
min_elements=4 * 1024,
max_elements=32 * 1024 ** 2,
density="density",
coalesced="coalesced",
dtype=dtype,
cuda=cuda,
),
],
seed=seed,
)