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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
# pylint: disable=too-few-public-methods,invalid-name,unused-argument,arguments-differ
# pylint: disable=consider-using-enumerate,too-many-lines
"""
Template configuration space.
Each template function can be parametrized by a ConfigSpace.
The space is declared when we invoke the template function with ConfigSpace.
During evaluation, we pass in a ConfigEntity, which contains a specific
entity in the space. This entity contains deterministic parameters.
"""
from __future__ import absolute_import as _abs
import itertools
import functools
import math
from collections import namedtuple, OrderedDict
import numpy as np
from tvm import schedule, thread_axis
from tvm.autotvm.util import get_const_int
Axis = namedtuple('Axis', ['space', 'index'])
try:
_long = long
except NameError:
_long = int
class InstantiationError(ValueError):
"""Actively detected error in instantiating a template with a config,
raised by cfg.raise_error
e.g. too many unrolling, too many threads in a block
"""
class TransformSpace(object):
"""Base class for transform space
TransformSpace is the node in the computation graph of axes
Note
----
We can regard our schedule code as a transformation graph of axes.
Starting from raw axes in the definition of tvm.compute, we can transform these axes
by some operators. The operator includes 'split', 'reorder' and 'annotate'.
Each operator has some tunable parameters (e.g. the split factor).
Then the tuning process is just to find good parameters of these op.
So the all the combinations of the parameters of these op forms our search space.
Naming convention:
We call the set of all possible values as XXXSpace. (XXX can be Split, Reorder, Config ...)
We call a specific entity in a space as XXXEntity.
"""
def __init__(self):
self.ins = []
self.num_output = 0
self.entities = []
def __len__(self):
return len(self.entities)
def __getitem__(self, index):
"""Get an entity of the space by index
Parameters
----------
index: int
Returns
-------
transform entity
"""
return self.entities[index]
@staticmethod
def get_num_output():
"""get number of output axes after this transform
Returns
-------
n: int
number of output axes
"""
return 0
class VirtualAxis(TransformSpace):
"""Axis placeholder in template
Parameters
----------
var: int or tvm.schedule.IterVar
If is int, return a virtual axis whose length is the provided argument.
If is IterVar, return a virtual axis whose length is extracted from
the IterVar's extent domain.
name: str
"""
name_ct = 0
def __init__(self, var, name=None):
super(VirtualAxis, self).__init__()
self.num_output = 1
if name is None:
name = 'axis_%d' % VirtualAxis.name_ct
VirtualAxis.name_ct += 1
self.name = name
if isinstance(var, (int, _long)):
self.length = var
elif isinstance(var, schedule.IterVar):
self.name = var.var.name
if var.dom is None:
self.length = -1
else:
self.length = get_const_int(var.dom.extent)
elif isinstance(var, VirtualAxis):
self.length = var.length
else:
raise RuntimeError("Invalid type of axis: " + str(type(var)))
@staticmethod
def get_num_output(var, name=None):
return 1
def __repr__(self):
return "vaxis(%s)" % self.name
def get_factors(n):
"""return all factors of an integer
Parameters
----------
n: int
integer to factorize
Returns
-------
factors: list
List of all factors
"""
step = 2 if n % 2 else 1
ret = list(set(
functools.reduce(
list.__add__, ([i, n//i] for i in range(1, int(math.sqrt(n)) + 1, step)
if n % i == 0))))
ret.sort()
return ret
def get_pow2s(n):
"""return all power-of-two numbers that are less or equal than the integer
Parameters
----------
n: int
integer for reference
Returns
-------
factors: list
List of all power-of-two numbers
"""
return [2**x for x in range(math.floor(math.log2(n)) + 1)]
class SplitSpace(TransformSpace):
"""Split an axis for several times"""
def __init__(self, axes, policy, **kwargs):
super(SplitSpace, self).__init__()
axis = axes[0]
self.policy = policy
self.entities = []
max_factor = kwargs.get("max_factor", 1 << 31)
fil = kwargs.get("filter", lambda x: True)
self.product = axis.length
self.num_output = kwargs.get("num_outputs", 0)
assert self.num_output > 0
if policy == 'candidate':
for size in kwargs["candidate"]:
assert len(size) == self.num_output
self.entities.append(SplitEntity(size))
else:
if policy == 'verbose':
# Include factors and power-of-twos. May generate tails.
divisibles = get_factors(self.product)
pow2s = get_pow2s(self.product)
factors = [x for x in list(set(divisibles) | set(pow2s)) if x <= max_factor]
elif policy == 'factors':
# Include divisible factors. Guarantee no tails.
factors = [x for x in get_factors(self.product) if x <= max_factor]
elif policy == 'power2':
# Include less, equal, and round-up power-of-two numbers. May generate tails.
factors = [x for x in get_pow2s(self.product) if x <= max_factor]
else:
raise RuntimeError("Invalid policy: %s" % policy)
# Enforce the product of all split factors equals to the axis length
no_tail = kwargs.get("no_tail", policy == 'factors')
# Generate split entity by enumerating candidate factors.
self.factors = factors
self._generate_space(0, [None] * (self.num_output - 1), enforce_no_tail=no_tail)
self.entities = list(filter(fil, self.entities))
def _generate_space(self, now, tmp_stack, enforce_no_tail=False):
"""Generate space by DFS"""
if now == self.num_output - 1:
if not enforce_no_tail or self.product % np.prod(tmp_stack, dtype=np.int64) == 0:
self.entities.append(SplitEntity([-1] + tmp_stack[::-1]))
else:
for factor in self.factors:
tmp_stack[now] = factor
self._generate_space(now + 1, tmp_stack, enforce_no_tail)
@staticmethod
def get_num_output(axes, policy, **kwargs):
return kwargs["num_outputs"]
def __repr__(self):
return ("Split(policy=%s, product=%d, num_outputs=%d) len=%d" %
(self.policy, self.product, self.num_output, len(self)))
class SplitEntity(object):
"""
A split operation with detailed parameters
that can apply to an axis
Parameters
----------
size: Array of int
the size of every axis after split
e.g. an axis of extent 128, we split it into 3 axes, a possible
size is [4, 4, 8] (4x4x8 = 128)
"""
def __init__(self, size):
self.size = size
def apply(self, sch, op, axis):
"""Apply split to an axis
Parameters
----------
sch: tvm.schedule.Schedule
The tvm schedule
op: tvm.tensor.Operation
The stage to be applied
axis: tvm.schedule.IterVar
axis to split
Returns
-------
axes : list of Axis
The transformed axes.
"""
ret = []
for i in range(1, len(self.size)):
ax0, ax1 = sch[op].split(axis, int(np.prod(self.size[i:])))
ret.append(ax0)
axis = ax1
return ret + [axis]
def __repr__(self):
return str(self.size)
class ReorderSpace(TransformSpace):
"""The parameter space for ordering an array of axes"""
def __init__(self, axes, policy, **kwargs):
super(ReorderSpace, self).__init__()
self.ins = axes
self.policy = policy
self.num_output = len(axes)
if policy == 'identity':
self.entities = [ReorderEntity(range(len(axes)))]
elif policy == 'all':
self.entities = [
ReorderEntity(x) for x in itertools.permutations(range(len(axes)))]
elif policy == 'interval_all':
begin, end = kwargs['interval']
sub_space = list(itertools.permutations(range(begin, end)))
prefix, suffix = tuple(range(begin)), tuple(range(end, len(axes)))
self.entities = [ReorderEntity(prefix + x + suffix) for x in sub_space]
elif policy == 'candidate':
candidate = kwargs["candidate"]
for can in candidate:
perm = [axes.index(x) for x in can]
self.entities.append(ReorderEntity(perm))
elif policy == 'interleave':
spatial, reduce = kwargs['spatial'], kwargs['reduce']
spatial = [[axes.index(x) for x in ch] for ch in spatial]
reduce = [[axes.index(x) for x in ch] for ch in reduce]
outer_merged = self._merge_chain([x[:-1] for x in spatial])
inner_merged = self._merge_chain([x[-1:] for x in spatial] + reduce)
for o in outer_merged:
for i in inner_merged:
self.entities.append(ReorderEntity(o + i))
elif policy == 'interleave_cuda':
spatial, reduce = kwargs['spatial'], kwargs['reduce']
spatial = [[axes.index(x) for x in ch] for ch in spatial]
reduce = [[axes.index(x) for x in ch] for ch in reduce]
outer_merged = self._merge_chain([x[:-1] for x in spatial])
reduce_merged = self._merge_chain(reduce)
inner_merged = [x[-1] for x in spatial]
for o in outer_merged:
for r in reduce_merged:
self.entities.append(ReorderEntity(o + r + inner_merged))
else:
raise RuntimeError("Invalid policy: " + policy)
@staticmethod
def get_num_output(axes, policy, **kwargs):
return len(axes)
def __repr__(self):
return "Reorder(policy=%s) len=%d" % (self.policy, len(self))
def _merge_chain(self, chains):
"""generate all combinations of merge some chains"""
merged = []
tmp_pt = [0] * len(chains)
tmp_stack = []
size = np.sum([len(x) for x in chains])
self._merge_dfs(chains, size, tmp_pt, tmp_stack, merged)
return merged
def _merge_dfs(self, chains, size, tmp_pt, tmp_stack, merged):
if np.sum(tmp_pt) == size:
merged.append(list(tmp_stack))
return
for i in range(len(chains)):
# use i == np.argmax(....) here to take spatial order into consideration
# if we don't want to consider spatial order, we can use tmp_pt[i] == np.max(....)
if (tmp_pt[i] < len(chains[i]) and
(i == np.argmax([len(chains[x]) - tmp_pt[x] for x in range(len(chains))]))):
tmp_stack.append(chains[i][tmp_pt[i]])
tmp_pt[i] += 1
self._merge_dfs(chains, size, tmp_pt, tmp_stack, merged)
tmp_pt[i] -= 1
tmp_stack.pop()
class ReorderEntity(object):
"""A reorder operation with detailed parameters that can apply to axes
Parameters
----------
perm: Array of int
define the permutation
"""
def __init__(self, perm):
self.perm = perm
def apply(self, sch, op, axes):
"""Apply reorder to an array of axes
Parameters
----------
sch: tvm.schedule.Schedule
The tvm schedule
op: tvm.tensor.Operation
The stage to be applied
axis: tvm.schedule.IterVar
axis to split
Returns
-------
axes : list of Axis
The transformed axes.
"""
if len(axes) == len(self.perm):
new_order = [axes[i] for i in self.perm]
else:
new_order = [axes[i] for i in self.perm if i < len(axes)]
sch[op].reorder(*new_order)
return new_order
def __repr__(self):
return str(self.perm)
class AnnotateSpace(TransformSpace):
"""The parameter space for annotating an array of axes"""
def __init__(self, axes, policy, **kwargs):
super(AnnotateSpace, self).__init__()
self.ins = axes
self.policy = policy
self.num_output = len(axes)
if policy == 'bind_gpu':
self.num_axis = len(axes)
if self.num_axis >= 6:
self.entities.append(AnnotateEntity(
['fuse'] * (self.num_axis - 6) +
['blockIdx.z', 'blockIdx.y', 'blockIdx.x',
'threadIdx.z', 'threadIdx.y', 'threadIdx.x']))
elif self.num_axis >= 4:
self.entities.append(AnnotateEntity(
['fuse'] * (self.num_axis - 4) +
['blockIdx.y', 'blockIdx.x',
'threadIdx.y', 'threadIdx.x']))
elif self.num_axis >= 2:
self.entities.append(AnnotateEntity(
['fuse'] * (self.num_axis - 2) +
['blockIdx.x', 'threadIdx.x']))
else:
raise RuntimeError("Unhandled case in bind_gpu")
elif policy == 'bind_gpu_virtual':
self.num_axis = len(axes)
if self.num_axis >= 9:
self.entities.append(AnnotateEntity(
['fuse'] * (self.num_axis - 9) +
['blockIdx.z', 'blockIdx.y', 'blockIdx.x',
'vthread', 'vthread', 'vthread',
'threadIdx.z', 'threadIdx.y', 'threadIdx.x']))
elif self.num_axis >= 6:
self.entities.append(AnnotateEntity(
['fuse'] * (self.num_axis - 6) +
['blockIdx.y', 'blockIdx.x',
'vthread', 'vthread',
'threadIdx.y', 'threadIdx.x']))
elif self.num_axis >= 3:
self.entities.append(AnnotateEntity(
['fuse'] * (self.num_axis - 3) +
['blockIdx.x', 'vthread', 'threadIdx.x']))
else:
raise RuntimeError("Unhandled case in bind_gpu")
elif policy == 'locate_cache':
self.num_axis = len(axes)
num_anchor = kwargs["num_anchor"]
self.anns = list(itertools.combinations(range(self.num_axis), num_anchor))
self.entities = [AnnotateEntity(x) for x in self.anns]
else: # none, vec, unroll, try_vec, try_unroll, try_vec_unroll, ...
anns = policy.replace('try', 'none').split('_')
for ann in anns:
if ann not in ['none', 'unroll', 'vec']:
raise RuntimeError("Invalid policy: " + policy)
self.num_axis = len(axes)
self.anns = [anns] * self.num_axis
self._generate_space(0, [""] * self.num_axis)
def _generate_space(self, now, tmp_stack):
"""Generate space by DFS"""
if now == self.num_axis:
# only vectorize inner most dimension
vec_ct = tmp_stack.count('vec')
if vec_ct in (0, 1):
self.entities.append(AnnotateEntity(list(tmp_stack)))
else:
for ann in self.anns[now]:
tmp_stack[now] = ann
self._generate_space(now + 1, tmp_stack)
@staticmethod
def get_num_output(axes, policy, **kwargs):
return len(axes)
def __repr__(self):
return "Annotate(policy=%s) len=%d" % (self.policy, len(self))
class AnnotateEntity(object):
"""An annotation operation with detailed parameters that can apply to axes
Parameters
----------
anns: Array of string
The annotations of axes
"""
def __init__(self, anns):
self.anns = anns
def apply(self, sch, op, axes, axis_lens=None,
max_unroll=None, vec_size=None, cfg=None, source=None):
"""Apply annotation to an array of axes
Parameters
----------
sch: tvm.schedule.Schedule
The tvm schedule
op: tvm.tensor.Operation
The stage to be applied
axes: Array of tvm.schedule.IterVar
axis to split
axis_lens: Array of int, optional
the length of axes
max_unroll: int, optional
maximum unroll step
vec_size: Array of int, optional
valid vector lanes for vectorization
cfg: ConfigEntity, optional
cfg for recording error
source: Array of Array tensor, optional
source tensor for attaching cache
Returns
-------
axes : list of tvm.schedule.IterVar
The transformed axes
"""
if source is not None: # special case : attach cache_read/cache_write
for src, to in zip(source, self.anns):
for t in src:
sch[t].compute_at(sch[op], axes[to])
else: # other cases
for i, ann in enumerate(self.anns):
if ann == 'none':
pass
elif ann == 'unroll':
if max_unroll and axis_lens[i] > max_unroll:
cfg.raise_error("Too large factor for unrolling")
sch[op].unroll(axes[i])
elif ann == 'vec':
if vec_size and axis_lens[i] not in vec_size:
cfg.raise_error("Wrong size of lanes in vectorization")
sch[op].vectorize(axes[i])
elif ann == 'blockIdx.x':
sch[op].bind(axes[i], thread_axis('blockIdx.x'))
elif ann == 'blockIdx.y':
sch[op].bind(axes[i], thread_axis('blockIdx.y'))
elif ann == 'blockIdx.z':
sch[op].bind(axes[i], thread_axis('blockIdx.z'))
elif ann == 'threadIdx.x':
sch[op].bind(axes[i], thread_axis('threadIdx.x'))
elif ann == 'threadIdx.y':
sch[op].bind(axes[i], thread_axis('threadIdx.y'))
elif ann == 'threadIdx.z':
sch[op].bind(axes[i], thread_axis('threadIdx.z'))
elif ann == 'vthread':
sch[op].bind(axes[i], thread_axis("vthread"))
elif ann == 'fuse':
assert i < len(axes) - 1
axes[i+1] = sch[op].fuse(axes[i], axes[i+1])
else:
raise RuntimeError("Invalid annotation " + ann)
return axes
def __repr__(self):
return str(self.anns)
class OtherOptionSpace(TransformSpace):
"""The parameter space for general option"""
def __init__(self, axes, policy, **kwargs):
super(OtherOptionSpace, self).__init__()
candidate = kwargs["candidate"]
self.entities = [OtherOptionEntity(x) for x in candidate]
@staticmethod
def get_num_output(axes, policy, **kwargs):
return 0
def __repr__(self):
return "OtherOption(%s) len=%d" % (self.entities, len(self))
class OtherOptionEntity(object):
"""The parameter entity for general option, with a detailed value"""
def __init__(self, val):
self.val = val
def __repr__(self):
return str(self.val)
class ConfigSpace(object):
"""The configuration space of a schedule. Pass it as config in template to
collect transformation space and build transform graph of axes
"""
def __init__(self):
# private dict to provide sugar
self.space_map = OrderedDict() # name -> space
self._collect = True
self._length = None
self._entity_map = OrderedDict() # name -> entity
self._constraints = []
self.errors = []
self.template_key = None
self.code_hash = None
self.flop = 0
self.is_fallback = False
@staticmethod
def axis(var):
"""get a virtual axis (axis placeholder)
Parameters
----------
var: int or tvm.schedule.IterVar
If is int, return an axis whose length is the provided argument.
If is IterVar, return an axis whose length is extracted from the
IterVar's extent domain.
"""
return VirtualAxis(var)
reduce_axis = axis
def define_split(self, name, axis, policy='factors', **kwargs):
"""Define a new tunable knob which splits an axis into a list of axes
Parameters
----------
name: str
name to index the entity of this space
axis: tvm.schedule.IterVar
axis to split
policy: str
name of policy.
If is 'factors', the tuner will try all divisible factors.
If is 'power2', the tuner will try power-of-two factors less or equal to the length.
If is 'verbose', the tuner will try all candidates in above two policies.
If is 'candidate', try given candidates.
kwargs: dict
extra arguments for policy
max_factor: int
the maximum split factor.
filter: function(int) -> bool
see examples below for how to use filter.
num_outputs: int
the total number of axis after split.
no_tail: bool
should we only include divisible numbers as split factors.
candidate: list
(policy=candidate) manual candidate list.
Examples
--------
>>> # use custom candidates
>>> cfg.define_split('tile_x', x, policy='candidate', candidate=[[1, 4, 4], [4, 1, 4]])
>>> # use a filter that only accepts the split scheme whose inner most tile is less then 4
>>> cfg.define_split('tile_y', y, policy='factors', filter=lambda x: x.size[-1] <= 4)
"""
axes = [axis]
return self._add_new_transform(SplitSpace, name, axes, policy, **kwargs)
def define_reorder(self, name, axes, policy, **kwargs):
"""Define a new tunable knob which reorders a list of axes
Parameters
----------
name: str
name to index the entity of this space
axes: Array of tvm.schedule.IterVar
axes to reorder
policy: str
name of policy
If is 'identity', do an identity permutation.
If is 'all', try all permutations.
If is 'interval_all', try all permutations of an interval of axes.
If is 'candidate', try listed candidate.
If is 'interleave', interleave chains of spatial axes and chains of reduction axes.
kwargs: dict
extra arguments for policy
"""
return self._add_new_transform(ReorderSpace, name, axes, policy, **kwargs)
def define_annotate(self, name, axes, policy, **kwargs):
"""Define a new tunable knob which annotates a list of axes
Parameters
----------
name: str
name to index the entity of this space
axes: Array of tvm.schedule.IterVar
axes to annotate
policy: str
name of policy
If is 'unroll', unroll the axes.
If is 'try_unroll', try to unroll the axes.
If is 'try_unroll_vec', try to unroll or vectorize the axes.
If is 'bind_gpu', bind the first few axes to gpu threads.
If is 'locate_cache', choose n axes to attach shared/local cache.
kwargs: dict
extra arguments for policy
"""
return self._add_new_transform(AnnotateSpace, name, axes, policy, **kwargs)
def define_knob(self, name, candidate):
"""Define a tunable knob with a list of candidates
Parameters
----------
name: str
name key of that option
candidate: list
list of candidates
"""
return self._add_new_transform(OtherOptionSpace, name, [], None, candidate=candidate)
def add_flop(self, flop):
"""Add float operation statistics for this tuning task
Parameters
---------
flop: int or float
number of float operations
"""
self.flop += flop
def raise_error(self, msg):
"""register error in config
Using this to actively detect error when scheudling.
Otherwise these error will occur during runtime, which
will cost more time.
Parameters
----------
msg: str
"""
self.errors.append(msg)
def valid(self):
"""Check whether the config meets all the constraints
Note: This check should be called after instantiation of task,
because the ConfigEntity/ConfigSpace collects errors during instantiation
Returns
-------
valid: bool
whether the config meets all the constraints
"""
return not bool(self.errors)
def _add_new_transform(self, space_class, name, axes, policy, **kwargs):
"""Add a new transform space in template"""
if self._collect:
# convert schedule axis to space definition axis
axes = [x if isinstance(x, (VirtualAxis, Axis)) else self.axis(x) for x in axes]
# add subspace (knob)
space = space_class(axes, policy, **kwargs)
self.space_map[name] = space
self._entity_map[name] = space[0]
return [Axis(space, i) for i in range(space.num_output)]
return [Axis(None, i) for i in range(space_class.get_num_output(axes, policy, **kwargs))]
def __len__(self):
if self._length is None:
self._length = int(np.prod([len(x) for x in self.space_map.values()]))
return self._length
def get(self, index):
"""Get a config entity with detailed parameters from this space
Parameters
----------
index: int
index in the space
"""
entities = OrderedDict()
t = index
for name, space in self.space_map.items():
entities[name] = space[t % len(space)]
t //= len(space)
ret = ConfigEntity(index, self.code_hash, self.template_key, entities, self._constraints)
return ret
def __iter__(self):
return self._entity_map.__iter__()
def __getitem__(self, name):
"""get the transform entity(knob) of this entity by name
do not use this to get a ConfigEntity of this space (should use ConfigSpace.get instead)
Parameters
----------
name: str
name of the transform
"""
return self._entity_map[name]
def __repr__(self):
res = "ConfigSpace (len=%d, space_map=\n" % len(self)
for i, (name, space) in enumerate(self.space_map.items()):
res += " %2d %s: %s\n" % (i, name, space)
return res + ")"
_ann_to_number = {
'none': 0, 'vec': 1, 'unroll': 2,
'blockIdx.x': 3, 'blockIdx.y': 4, 'blockIdx.z': 5,
'threadIdx.x': 6, 'threadIdx.y': 7, 'threadIdx.z': 8,
'vthread': 9, 'fuse': 10
}
class ConfigEntity(ConfigSpace):
"""A configuration with detailed parameters
Parameters
----------
index: int
index of this config in space
code_hash: str
hash of schedule code
template_key : str
The specific template key
entity_map: dict
map name to transform entity
constraints : list
List of constraints
"""
def __init__(self, index, code_hash, template_key, entity_map, constraints):
super(ConfigEntity, self).__init__()
self.index = index
self.template_key = template_key
self._collect = False
self._entity_map = entity_map
self._space_map = None
self._constraints = constraints
self.code_hash = code_hash
def get_flatten_feature(self):
""" flatten entities to a numerical one-dimensional feature vector
Returns
-------
fea: np.array
one dimensional float32 array
"""
fea = []
for _, v in self._entity_map.items():
if isinstance(v, SplitEntity):
fea.extend(v.size)
elif isinstance(v, ReorderEntity):
# use a naive way: directly copy the permutation
fea.extend(v.perm)
elif isinstance(v, AnnotateEntity):
# one-hot encoding
for ann in v.anns:
tmp = [0] * len(_ann_to_number)
tmp[_ann_to_number[ann]] = 1
fea.extend(tmp)
elif isinstance(v, OtherOptionEntity):
fea.append(v.val)
return np.array(fea, dtype=np.float32)
def get_other_option(self):
"""
Returns
-------
other_option: dict
other tunable parameters (tunable parameters defined by `cfg.define_knob`)
"""
return {x: x.val for x in self._entity_map.values() if isinstance(x, OtherOptionEntity)}
def to_json_dict(self):
"""convert to a json serializable dictionary
Return
------
json_dict: dict
a json serializable dictionary
"""
ret = {}
ret['i'] = int(self.index)
ret['t'] = self.template_key
ret['c'] = self.code_hash
entity_map = []
for k, v in self._entity_map.items():
if isinstance(v, SplitEntity):
entity_map.append((k, 'sp', v.size))
elif isinstance(v, ReorderEntity):
entity_map.append((k, 're', v.perm))
elif isinstance(v, AnnotateEntity):
entity_map.append((k, 'an', v.anns))
elif isinstance(v, OtherOptionEntity):
entity_map.append((k, 'ot', v.val))
else:
raise RuntimeError("Invalid entity instance: " + v)
ret['e'] = entity_map
return ret
@staticmethod
def from_json_dict(json_dict):
"""Build a ConfigEntity from json serializable dictionary
Parameters
----------
json_dict: dict
Json serializable dictionary. This should be the return value
of :any:`to_json_dict`.
Returns
-------
config: ConfigEntity
The corresponding config object
"""
index = json_dict["i"]
code_hash = json_dict["c"]
template_key = json_dict["t"]
constraints = []
entity_map = OrderedDict()
for item in json_dict["e"]:
key, knob_type, knob_args = item
if knob_type == 'sp':
entity = SplitEntity(knob_args)
elif knob_type == 're':
entity = ReorderEntity(knob_args)
elif knob_type == 'an':
entity = AnnotateEntity(knob_args)
elif knob_type == 'ot':
entity = OtherOptionEntity(knob_args)
else:
raise RuntimeError("Invalid config knob type: " + knob_type)
entity_map[str(key)] = entity
return ConfigEntity(index, code_hash, template_key, entity_map, constraints)
def __repr__(self):
return "%s,%s,%s,%d" % (str(self._entity_map)[12:-1], self.template_key,
self.code_hash, self.index)
class FallbackConfigEntity(ConfigSpace):
"""The config entity created to support fallback"""
def __init__(self):
super(FallbackConfigEntity, self).__init__()
self.is_fallback = True
def fallback_split(self, name, constraints):
"""Fallback a split knob
Parameters
----------
name: str
name of the knob
constraints: List of int
The maximum tile size for every dimension. Value `-1` means no constraint.
Examples
--------
If you use cfg.define_split('tile_0', 128, num_outputs=3),
Then cfg.fallback_split('tile_0', [-1, 8, 4]) will give you cfg['tile_0'].size = [4, 8, 4]
If you use cfg.define_split('tile_0', 49, num_outputs=3),
Then cfg.fallback_split('tile_0', [-1, 8, 4]) will give you cfg['tile_0'].size = [7, 7, 1]
"""
space = self.space_map[name]
assert isinstance(space, SplitSpace)
assert len(constraints) == space.num_output
# '-1' means no constraint
constraints = [x if x != -1 else 1e10 for x in constraints]
entity = self._entity_map[name]
now = space.product
for i in reversed(range(space.num_output)):
factors = get_factors(now)
find = len(factors) - 1
for j, f in enumerate(factors):
if f > constraints[i]:
find = j - 1
break
if find >= 0:
entity.size[i] = factors[find]
now //= factors[find]
else:
raise RuntimeError("Cannot find feasible fallback split entity for node: " + name)
def fallback_with_reference_log(self, ref_log):
"""A data driven fallback mechanism.
We use tuned parameters from TopHub as reference data.
For an unseen shape, we find the most similar tuned one from TopHub and
mimic its parameters.
Parameters
----------
ref_log: List of (MeasureInput, MeasureResult)
The reference log
"""
knob_names = [x for x in self.space_map.keys() if
isinstance(self.space_map[x], SplitSpace)]
# find best match config in reference data by matching tiling factors
factor_list = []
for knob_name in knob_names:
factor_list.append(get_factors(self.space_map[knob_name].product))
best_match_cfg = None
best_match_score = 0
for inp, _ in ref_log:
match_score = 0
for i, knob_name in enumerate(knob_names):
factors = get_factors(int(np.prod(inp.config[knob_name].size)))
match_score += (float(len(set(factor_list[i]).intersection(factors))) /
len(factor_list[i]))
if match_score > best_match_score:
best_match_score, best_match_cfg = match_score, inp.config
if best_match_cfg is None:
return
# mimic its tiling strategy
for knob_name in knob_names:
constraint = list(best_match_cfg[knob_name].size)
constraint[0] = -1
self.fallback_split(knob_name, constraint)
# copy other knobs
for knob_name in self.space_map.keys():
if not isinstance(self.space_map[knob_name], SplitSpace):
self._entity_map[knob_name] = best_match_cfg[knob_name]
def __setitem__(self, name, entity):
"""set the entity(knob) of by name
Parameters
----------
name: str
name of the entity
entity: SplitEntity, ReorderEntity, AnnotateEntity, OtherOptionEntity
value of the entity
"""
self._entity_map[name] = entity
def __repr__(self):
return "%s,%s,%s" % (str(self._entity_map)[12:-1], self.template_key, self.code_hash)
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