[AUTOTVM] Core part of auto-tuning module (#1312)

CMakeLists.txt

@@ -96,6 +96,7 @@ assign_source_group("Include" ${GROUP_INCLUDE})
 file(GLOB COMPILER_SRCS
     src/api/*.cc
     src/arithmetic/*.cc
+    src/autotvm/*.cc
     src/codegen/*.cc
     src/codegen/stack_vm/*.cc
     src/lang/*.cc

docs/api/python/autotvm.rst

+tvm.autotvm
+-----------
+.. automodule:: tvm.autotvm
+
+tvm.autotvm.measure
+~~~~~~~~~~~~~~~~~~~
+.. automodule:: tvm.autotvm.measure.measure
+
+.. autoclass:: tvm.autotvm.measure.MeasureInput
+    :members:
+
+.. autoclass:: tvm.autotvm.measure.MeasureResult
+    :members:
+
+.. autofunction:: tvm.autotvm.measure.measure_option
+
+.. autofunction:: tvm.autotvm.measure.create_measure_batch
+
+
+tvm.autotvm.tuner
+~~~~~~~~~~~~~~~~~
+.. automodule:: tvm.autotvm.tuner
+    :members:
+
+.. autoclass:: tvm.autotvm.tuner.Tuner
+    :members:
+
+.. autoclass:: tvm.autotvm.tuner.RandomTuner
+    :members:
+    :inherited-members:
+
+.. autoclass:: tvm.autotvm.tuner.GridSearchTuner
+    :members:
+    :inherited-members:
+
+.. autoclass:: tvm.autotvm.tuner.GATuner
+    :members:
+    :inherited-members:
+
+.. autoclass:: tvm.autotvm.tuner.XGBTuner
+    :members:
+    :inherited-members:
+
+.. automodule:: tvm.autotvm.tuner.callback
+    :members:
+
+tvm.autotvm.task
+~~~~~~~~~~~~~~~~
+.. automodule:: tvm.autotvm.task
+    :members:
+
+.. automodule:: tvm.autotvm.task.task
+    :members:
+
+.. automodule:: tvm.autotvm.task.space
+    :members:
+
+tvm.autotvm.record
+~~~~~~~~~~~~~~~~~~
+.. automodule:: tvm.autotvm.record
+    :members:

docs/api/python/index.rst

@@ -14,6 +14,7 @@ Python API
    ndarray
    container
    function
+   autotvm
    graph_runtime
    rpc
    bridge

docs/conf.py

@@ -191,6 +191,7 @@ gallery_dirs = ["tutorials", "vta/tutorials"]
 subsection_order = ExplicitOrder(
     ['../tutorials/language',
      '../tutorials/optimize',
+     '../tutorials/autotvm',
      '../tutorials/vta',
      '../tutorials/topi',
      '../tutorials/deployment',

include/tvm/ir_pass.h

@@ -488,7 +488,7 @@ bool VerifyMemory(LoweredFunc func, int device_type);
  *
  *        "max_local_memory_per_block": Total amount of local memory per block (in bytes).
  *        "max_shared_memory_per_block": Total amount of shared memory per block (in bytes).
- *        "max_thread_per_block": Maximum number of threads per block.
+ *        "max_threads_per_block": Maximum number of threads per block.
  *        "max_thread_x": Maximum length of threadIdx.x.
  *        "max_thread_y": Maximum length of threadIdx.y.
  *        "max_thread_z": Maximum length of threadIdx.z.

python/tvm/autotvm/__init__.py

+"""The auto-tuning module of tvm
+
+This module includes:
+
+* Tuning space definition API
+
+* Efficient auto-tuners
+
+* Tuning result and database support
+
+* Distributed measurement to scale up tuning
+"""
+
+from . import database
+from . import feature
+from . import measure
+from . import record
+from . import task
+from . import tuner
+from . import util
+
+# some shortcuts
+from .measure import measure_option, MeasureInput, MeasureResult, MeasureErrorNo
+from .tuner import callback
+from .task import template, get_config, create, ConfigSpace, ConfigEntity
+from .record import ApplyHistoryBest as apply_history_best

python/tvm/autotvm/database.py

+# pylint: disable=consider-using-enumerate,invalid-name
+"""
+Database of MeasureInput/MeasureResult pair.
+This can be used for replaying measurement.
+"""
+import os
+
+from .record import encode, decode, measure_str_key
+
+
+class Database(object):
+    """
+    Base class for a record database object.
+    """
+    def load(self, inp, get_all=False):
+        """
+        Load a result based on an input's string key
+
+        Parameters
+        ----------
+        inp: MeasureInput
+            to be translated into key for RedisDB
+        get_all: bool, optional
+            Whether the latest result (or all matching results) should be returned
+
+        Returns
+        -------
+        rec: MeasureResult if previously saved, otherwise None
+        """
+        raise NotImplementedError()
+
+    def save(self, inp, res, extend=False):
+        """
+        Save a result based on an input's string key
+
+        Parameters
+        ----------
+        inp: MeasureInput
+            to be translated into key for RedisDB
+        res: MeasureResult
+            to associate with key
+        extend:
+            Whether to extend existing MeasureResults if they exist
+        """
+        raise NotImplementedError()
+
+
+def filter_inputs(db, measure_inputs, retry=False):
+    """
+    Filter a measure_inputs batch based on saved db results
+
+    Parameters
+    ----------
+    db: Database
+        database object
+    measure_inputs: Array of MeasureInput
+        measure_inputs as expected in measure_batch
+    retry: bool
+        whether to retry if the saved result is a failure
+
+    Returns
+    -------
+    partial_results: Array of MeasureResult
+        a full list of result, where None denotes no corresponding saved result
+    unsaved: Array of MeasureInput
+        a list that only contains unsaved inputs
+    """
+    partial_results = list()
+    unsaved = list()
+    for inp in measure_inputs:
+        res = db.load(inp)
+        if res is None or (retry and res.error_no != 0):
+            unsaved.append(inp)
+            partial_results.append(None)
+        else:
+            partial_results.append(res)
+    return partial_results, unsaved
+
+class RedisDatabase(Database):
+    """
+    Redis version of record database
+    """
+    REDIS_PROD = 15
+    REDIS_LOCA = 14
+    REDIS_TEST = 13        # for unit test
+    REDIS_NIGHT_TEMP = 12  # for nightly report (will be flushed after every workload)
+
+    MAGIC_SPLIT = "$"
+
+    def __init__(self, db_index=REDIS_PROD):
+        import redis
+
+        if db_index == RedisDatabase.REDIS_TEST:
+            host = 'localhost'
+        else:
+            host = os.environ.get('TVM_FLEET_HOST')
+        self.db = redis.StrictRedis(host=host, port=6379, db=db_index)
+        self.db_index = db_index
+
+    def set(self, key, value):
+        self.db.set(key, value)
+
+    def get(self, key):
+        return self.db.get(key)
+
+    def load(self, inp, get_all=False):
+        current = self.get(measure_str_key(inp))
+        if current is not None:
+            current = str(current)
+            records = [decode(x) for x in current.split(RedisDatabase.MAGIC_SPLIT)]
+            results = [rec[1] for rec in records]
+            if get_all:
+                return results
+            return max(results, key=lambda result: result.timestamp)
+        return current
+
+    def save(self, inp, res, extend=False):
+        current = self.get(measure_str_key(inp))
+        if not extend or current is None:
+            self.set(measure_str_key(inp),
+                     RedisDatabase.MAGIC_SPLIT.join([encode(inp, res)]))
+        else:
+            current = current.split(RedisDatabase.MAGIC_SPLIT)
+            self.set(measure_str_key(inp),
+                     RedisDatabase.MAGIC_SPLIT.join(current + [encode(inp, res)]))
+
+    def filter(self, func):
+        """
+        Dump all of the records for a particular target
+
+        Parameters
+        ----------
+        func: callable
+            The signature of the function is bool (MeasureInput, Array of MeasureResult)
+
+        Returns
+        -------
+        list of records (inp, result) matching the target
+
+        Examples
+        --------
+        get records for a target
+        >>> db.filter(lambda inp, resulst: "cuda" in inp.target.keys)
+        """
+        matched_records = list()
+        # may consider filtering in iterator in the future
+        for key in self.db:
+            current = self.get(key)
+            try:
+                records = [decode(x) for x in current.spilt(RedisDatabase.MAGIC_SPLIT)]
+            except TypeError:  # got a badly formatted/old format record
+                continue
+
+            inps, results = zip(*records)
+            inp = inps[0]
+            if not func(inp, results):
+                continue
+            result = max(results, key=lambda res: res.timestamp)
+            matched_records.append((inp, result))
+        return matched_records
+
+    def flush(self):
+        self.db.flushdb()
+
+class DummyDatabase(RedisDatabase):
+    """
+    A database based on python dictionary for testing.
+    """
+
+    def __init__(self):
+        # pylint: disable=super-init-not-called
+        self.db = {}
+
+    def set(self, key, value):
+        self.db[key] = value
+
+    def get(self, key):
+        return self.db.get(key)
+
+    def flush(self):
+        self.db = {}

python/tvm/autotvm/env.py

+"""Global configuration/variable scope for autotvm"""
+
+class AutotvmGlobalScope(object):
+    current = None
+
+    def __init__(self):
+        self._old = AutotvmGlobalScope.current
+        AutotvmGlobalScope.current = self
+
+        self.cuda_target_arch = None
+
+GLOBAL_SCOPE = AutotvmGlobalScope()

python/tvm/autotvm/feature.py

+# pylint: disable=invalid-name
+"""Extract feature of iter vars
+
+There are two types of feature
+1) Itervar feature
+   This feature is extracted based on loop variables.
+   Different loop structures will result in different shapes of feature
+2) Curve sample feature (relation feature)
+   This feature is extracted by sampling relation curve.
+   This feature is invariant of loop structure.
+"""
+
+import struct
+import numpy as np
+
+from tvm import schedule, ir_pass, build_module, get_global_func, target as _target
+
+def ana_lower(sch, args,
+              binds=None,
+              simple_mode=True):
+    """Do lower while keeping all axes in IR
+    i.e. Do not eliminate loop with extent of 1, do not vectorize, unroll or inject virtual threads
+    """
+    binds, _ = build_module.get_binds(args, binds)
+    sch = sch.normalize()
+    # Phase 0
+    bounds = schedule.InferBound(sch)
+    stmt = schedule.ScheduleOps(sch, bounds, True)
+    stmt = ir_pass.StorageFlatten(stmt, binds, 64)
+    stmt = ir_pass.CanonicalSimplify(stmt)
+    assert simple_mode
+    return stmt
+
+try:
+    _get_buffer_curve_sample_flatten = get_global_func(
+        "autotvm.feature.GetCurveSampleFeatureFlatten")
+    _get_itervar_feature = get_global_func("autotvm.feature.GetItervarFeature")
+    _get_itervar_feature_flatten = get_global_func("autotvm.feature.GetItervarFeatureFlatten")
+except ValueError as e:
+    def raise_error(*args, **kwargs):  # pylint: disable=unused-argument
+        raise RuntimeError("Cannot load autotvm c++ API")
+    _get_buffer_curve_sample_flatten = _get_itervar_feature = _get_itervar_feature_flatten = \
+        raise_error
+
+def get_itervar_feature(sch, args, take_log=False):
+    """get features of iter vars
+
+    Parameters
+    ----------
+    sch: tvm.schedule.Schedule
+    args: Array of tvm.tensor.Tensor
+        the buffer args for lower
+    take_log: bool
+        whether take log of numerical statics
+
+    Returns
+    -------
+    features of every axis in the IR, see doc/features.md for detail
+    """
+    stmt = ana_lower(sch, args, simple_mode=True)
+    feas = _get_itervar_feature(stmt, take_log)
+
+    # convert tvm node to python type
+    ret = []
+    for row in feas:
+        tmp = []
+        tmp.append([row[0][0].value, row[0][1]])
+        for item in row[1:]:
+            tmp.append([item[0].value] + [x.value for x in item[1:]])
+        ret.append(tmp)
+    return ret
+
+def flatten_itervar_feature(fea):
+    """flatten features into one-dimensional feature vectors
+
+    Parameters
+    ----------
+    fea: list
+        return value of get_itervar_feature
+
+    Returns
+    -------
+    flatten_feature: np.ndarray
+        one-dimensional vector
+    """
+    flatten = []
+    for axis in fea:
+        for pair in axis[1:]:
+            flatten.append(pair[1:])
+    return np.concatenate(flatten)
+
+def get_itervar_feature_flatten(sch, args, take_log=True):
+    """get flatten features of iter vars
+    this is equivalent to get_itervar_feature + flatten_itervar_feature, but much faster.
+
+    Parameters
+    ----------
+    sch: tvm.schedule.Schedule
+    args: Array of tvm.tensor.Tensor
+        the buffer args for lower
+    take_log: bool
+        whether take log of numerical statics
+
+    Returns
+    -------
+    flatten_feature: np.ndarray
+        one-dimensional vector
+    """
+    stmt = ana_lower(sch, args, simple_mode=True)
+    feas = _get_itervar_feature_flatten(stmt, take_log)
+    feas = struct.unpack('%df' % (len(feas)//4), feas)
+    return feas
+
+def get_flatten_name(fea):
+    """ Get names of feature after flatten.
+
+    Parameters
+    ----------
+    fea: list or str
+        return value of get_itervar_feature or a line of logfile
+
+    Returns
+    -------
+    feature_names: Array of str
+    """
+
+    feature_name = {
+        "_attr_": ["length", "nest_level", "topdown", "bottomup"] +
+                  ["ann_%d" % i for i in range(20)],
+        "_arith_": ["add", "mul", "div"],
+        "buf_touch": ["stride", "mod", "count", "reuse", "T_count", "T_reuse"],
+    }
+
+    if isinstance(fea, str):
+        from .record import decode
+        # flatten line to feature
+        line = fea
+        inp, _ = decode(line)
+        target = _target.create(inp.target)
+        with target:
+            s, args = inp.template.instantiate(inp.config)
+        fea = get_itervar_feature(s, args)
+
+    names = []
+    ct = 0
+    for row in fea:
+        var_name = str(row[0][1])
+        for pair in row[1:]:
+            key = pair[0]
+            if key in feature_name:
+                name_list = feature_name[key]
+            else:
+                name_list = feature_name["buf_touch"]
+
+            for i in range(len((pair[1:]))):
+                names.append(".".join(["f%d" % ct, var_name, key, name_list[i]]))
+                ct += 1
+    return names
+
+
+def get_buffer_curve_sample_flatten(sch, args, sample_n=30):
+    """
+    Get flatten curve sample feature (relation feature)
+
+    Parameters
+    ----------
+    sch: tvm.schedule.Schedule
+    args: Array of tvm.tensor.Tensor
+        the buffer args for lower
+    sample_n: int
+        number of sample points along one dimension
+
+    Returns
+    -------
+    flatten_feature: np.ndarray
+        one-dimensional vector
+    """
+    stmt = ana_lower(sch, args, simple_mode=True)
+    feas = _get_buffer_curve_sample_flatten(stmt, sample_n, False)
+    feas = struct.unpack('%df' % (len(feas)//4), feas)
+    return feas

python/tvm/autotvm/measure/__init__.py

+"""Distributed executor infrastructure to scale up the tuning"""
+
+from .measure import MeasureInput, MeasureResult, MeasureErrorNo
+from .measure import create_measure_batch, measure_option
+from .measure_methods import request_remote
+
+from .local_executor import LocalExecutor
+from .executor import Future, Executor

python/tvm/autotvm/measure/executor.py

+""" Abstraction for asynchronous job execution """
+
+class Executor(object):
+    """
+    Base abstract executor interface for asynchronous job submission.
+    Allows submit asynchronous jobs and returns the Future object.
+    """
+    # timeout for jobs that may hang
+    DEFAULT_TIMEOUT = 60
+
+    def submit(self, func, *args, **kwargs):
+        """
+        Pass task (function, arguments) to the Executor.
+
+        Parameters
+        ----------
+        func : callable
+            function to be run by a worker
+        args : list or tuple, optional
+            arguments passed to the function
+        kwargs : dict, optional
+            The keyword arguments
+
+        Returns
+        -------
+        future : Future
+            Future object wrapping the task which can be used to
+            collect the task's result.
+        """
+        raise NotImplementedError()
+
+
+class Future(object):
+    """
+    Base class of the future object.
+    The implementations can return object of subclass of this.
+    This objects encapsulates the asynchronous execution of task
+    submitted to another thread, or another worker for execution.
+
+    Future objects store the state of tasks--can be polled for
+    result or a blocking call to retrieve the result can be used.
+    """
+    def done(self):
+        """
+        Return True if job was successfully cancelled or finished running.
+        """
+        raise NotImplementedError()
+
+    def get(self, timeout=None):
+        """
+        Get the result. This will block until the result is available.
+
+        Parameters
+        ----------
+        timeout : int or float, optional
+            Maximum number of seconds to wait before it timeouts.
+            If not specified, it means we block until the result is available.
+
+        Returns
+        -------
+        result : Any
+            The result returned by the submitted function.
+
+        Raises
+        ------
+        TimeoutError : if the result call timeouts.
+        """
+        raise NotImplementedError()
+
+class FutureError(RuntimeError):
+    """Base error class of all future events"""
+    pass
+
+# pylint:disable=redefined-builtin
+class TimeoutError(FutureError):
+    """Error raised when a task is timeout."""
+    pass
+
+class ExecutionError(FutureError):
+    """
+    Error raised when future execution crashes or failed.
+    """
+    pass

python/tvm/autotvm/measure/local_executor.py

+"""Local based implementation of the executor using multiprocessing"""
+
+import signal
+
+from multiprocessing import Process, Queue
+try:
+    from queue import Empty
+except ImportError:
+    from Queue import Empty
+
+import psutil
+
+from . import executor
+
+
+def kill_child_processes(parent_pid, sig=signal.SIGTERM):
+    """kill all child processes recursively"""
+    try:
+        parent = psutil.Process(parent_pid)
+    except psutil.NoSuchProcess:
+        return
+    children = parent.children(recursive=True)
+    for process in children:
+        try:
+            process.send_signal(sig)
+        except psutil.NoSuchProcess:
+            return
+
+def _execute_func(func, queue, args, kwargs):
+    """execute function and return the result or exception to a queue"""
+    try:
+        res = func(*args, **kwargs)
+    except Exception as exc:  # pylint: disable=broad-except
+        res = exc
+    queue.put(res)
+
+def timeout_monitor(queue, timeout, func, args, kwargs):
+    """A wrapper to support timeout of a function call"""
+
+    # start a new process for timeout (cannot use thread because we have c function)
+    p = Process(target=_execute_func, args=(func, queue, args, kwargs))
+    p.start()
+    p.join(timeout=timeout)
+
+    alive = p.is_alive()
+    kill_child_processes(p.pid)
+    p.terminate()
+    p.join()
+
+    if alive:
+        queue.put(executor.TimeoutError())
+    else:
+        if queue.empty():
+            queue.put(executor.ExecutionError("Fatal error in local executor"))
+
+
+class LocalFuture(executor.Future):
+    """Local wrapper for the future
+
+    Parameters
+    ----------
+    process: multiprocessing.Process
+        process for running this task
+    queue: multiprocessing.Queue
+        queue for receiving the result of this task
+    """
+    def __init__(self, process, queue):
+        self._done = False
+        self._process = process
+        self._queue = queue
+
+    def done(self):
+        self._done = self._done or not self._queue.empty()
+        return self._done
+
+    def get(self, timeout=None):
+        try:
+            res = self._queue.get(block=True, timeout=timeout)
+        except Empty:
+            raise executor.TimeoutError()
+        if self._process.is_alive():
+            kill_child_processes(self._process.pid)
+            self._process.terminate()
+        self._process.join()
+        self._queue.close()
+        self._queue.join_thread()
+        self._done = True
+        del self._queue
+        del self._process
+        return res
+
+
+class LocalFutureNoFork(executor.Future):
+    """Local wrapper for the future.
+    This is a none-fork version of LocalFuture.
+    Use this for the runtime that does not support fork (like cudnn)
+    """
+    def __init__(self, result):
+        self._result = result
+
+    def done(self):
+        return True
+
+    def get(self, timeout=None):
+        return self._result
+
+
+class LocalExecutor(executor.Executor):
+    """Local executor that runs workers on the same machine with multiprocessing."""
+    def __init__(self, timeout=None):
+        self.timeout = timeout or executor.Executor.DEFAULT_TIMEOUT
+
+    def submit(self, func, *args, **kwargs):
+        """
+
+        Note
+        ----------
+        By default, the executor will fork a new process for a new job
+        But some runtime does not support fork (e.g. cuda runtime, cudnn).
+        In this circumstance, you should set 'fork_new_process' to False in kwargs
+        """
+        fork_new_process = kwargs.pop('fork_new_process', True)
+
+        if not fork_new_process:
+            return LocalFutureNoFork(func(*args, **kwargs))
+
+        queue = Queue(1)
+        process = Process(target=timeout_monitor,
+                          args=(queue, self.timeout, func, args, kwargs))
+        process.start()
+        return LocalFuture(process, queue)

python/tvm/autotvm/measure/measure.py

+# pylint: disable=pointless-string-statement,consider-using-enumerate,invalid-name
+"""User facing API for specifying how to measure the generated code"""
+import time
+from collections import namedtuple
+
+import numpy as np
+
+from ... import build, nd, target as _target
+from ...contrib.util import tempdir
+from ...rpc.tracker import Tracker
+from ...rpc.server import Server
+
+from ..util import get_const_tuple
+from .local_executor import LocalExecutor
+
+
+class MeasureInput(namedtuple("MeasureInput", ["target", "task", "config"])):
+    """
+    Stores all the necessary inputs for a measurement.
+
+    Parameters
+    ----------
+    target : tvm.target.Target
+        The target device
+    task : task.Task
+        Task function
+    config : ConfigEntity
+        Specific configuration.
+    """
+
+class MeasureResult(namedtuple("MeasureResult", ["costs", "error_no", "all_cost", "timestamp"])):
+    """
+    Stores all the results of a measurement
+
+    Parameters
+    ----------
+    costs: Array of float or Array of Exception
+        If no error occurs for this measurement, it is an array of measured running times.
+        If some error occurs during the measurement, it is an array of the exception objections.
+    error_no: int
+        Denote error type, defined by MeasureErrorNo
+    all_cost: float
+        All cost of this measure, including rpc, compilation, test runs
+    timestamp: float
+        The absolute time stamp when we finish measurement.
+    """
+
+class MeasureErrorNo(object):
+    """Error type for MeasureResult"""
+    NO_ERROR = 0              # no error
+    INSTANTIATION_ERROR = 1   # error when calling template function
+    COMPILE_HOST = 2          # error when compiling code on host (e.g. tvm.build)
+    COMPILE_DEVICE = 3        # error when compiling code on device (e.g. opencl JIT on device)
+    RUNTIME_DEVICE = 4        # error when run program on device
+    WRONG_ANSWER = 5          # answer is wrong when compared to a golden output
+    FLEET_ERROR = 6           # error of measure infrastructure
+
+
+def measure_option(mode,
+                   number=1,
+                   repeat=1,
+                   timeout=60,
+                   parallel_num=1,
+                   pack_size=1,
+                   check_correctness=False,
+                   build_option=None,
+                   replay_db=None,
+                   save_to_replay_db=True,
+                   rpc_device_key=None,
+                   rpc_priority=1,
+                   rpc_timeout=60,
+                   rpc_tracker_addr=None,
+                   use_ndk=False,
+                   custom_measure_batch=None):
+    """Configure how to do measurement
+
+    Parameters
+    ----------
+    mode: str
+        'local': use the local device for measurement. In this mode,
+        the tuner starts a tracker and a RPC server silently for the user.
+
+        'rpc': request devices for measurement from rpc tracker. In this mode,
+        you should start a rpc tracker in a separate processing.
+
+        'custom': use custom measure function
+
+        'local-nofork': use local device for measure but does not use multiprocessing.
+        This mode is suitable for debug, but does not support timeout and parallel.
+
+    number : int, optional
+        Number of times to do the measurement for average
+    repeat : int, optional
+        Number of times to repeat the measurement.
+        In total, the generated code will be run (1 + number x repeat) times,
+        where the first one is warm up. The returned result contains `repeat` costs,
+        each of which is the average of `number` test run.
+    timeout: int, optional
+        Timeout for a whole batch. TimeoutError will be returned as the result if a
+        task timeouts.
+    parallel_num: int, optional
+        The number of measurement task that can run in parallel.
+        Set this according to the number of cpu cores (for compilation) and
+        the number of devices you have (for measuring generate code).
+    pack_size : int, optional
+        Number of configs to measure in one RPC call.
+        Usually this can be set to 1. If your device has high cost to establish a rpc connection,
+        set this higher.
+    check_correctness: bool
+        Whether check correctness after measurement.
+    build_option: Dict, optional
+        Build options for tvm.build_config
+
+    replay_db : Database, optional
+        The database that we retrieve saved MeasureResults from
+    save_to_replay_db: bool, optional
+        Whether save measure result to database. This is useless when replay_db is None
+
+    rpc_priority: int, optional
+        Priority of this task, used by scheduler in tracker
+    rpc_device_key: str, optional
+        The device key of registered devices in tracker
+    rpc_timeout: int, optional
+        Timeout of rpc session
+    rpc_tracker_addr: Tuple(str, int), optional
+        The address of rpc tracker in Tuple(host, port) format.
+        If is set, will use this address.
+        If is not set, will use environment variable "TVM_TRACKER_HOST" and "TVM_TRACKER_PORT"
+
+    use_ndk: bool, option
+        Whether export requires ndk
+    custom_measure_batch: callable, optional
+        custom measure function
+
+    Returns
+    -------
+    options: dict
+        A dict to store all options
+    """
+    return {
+        'mode': mode,
+        'number': number,
+        'repeat': repeat,
+        'timeout': timeout,
+        'parallel_num': parallel_num,
+        'pack_size': pack_size,
+        'check_correctness': check_correctness,
+        'build_option': build_option,
+
+        'replay_db': replay_db,
+        'save_to_replay_db': save_to_replay_db,
+
+        'rpc_device_key': rpc_device_key,
+        'rpc_priority': rpc_priority,
+        'rpc_timeout': rpc_timeout,
+        'rpc_tracker_addr': rpc_tracker_addr,
+
+        'use_ndk': use_ndk,
+        'custom_measure_batch': custom_measure_batch
+    }
+
+
+def create_measure_batch(task, options):
+    """Get a standard measure_batch function.
+
+    Parameters
+    ----------
+    task: tvm.autotvm.task.Task
+        The tuning task
+    options: dict
+        The option for measuring generated code.
+        You should use the return value of :any:`autotvm.measure_option` for this argument
+
+    Returns
+    -------
+    measure_batch: callable
+        a callback function to measure a batch of configs
+    """
+    from . import measure_methods
+    from ..database import filter_inputs
+
+    mode = options['mode']
+    number, repeat = options['number'], options['repeat']
+    timeout, parallel_num = options['timeout'], options['parallel_num']
+    pack_size = options['pack_size']
+    check_correctness = options['check_correctness']
+    build_option = options['build_option']
+    replay_db = options['replay_db']
+    save_to_replay_db = options['save_to_replay_db']
+    rpc_device_key = options['rpc_device_key']
+    rpc_priority, rpc_timeout = options['rpc_priority'], options['rpc_timeout']
+    use_ndk = options['use_ndk']
+    custom_measure_batch = options['custom_measure_batch']
+
+    kwargs = {}
+    executor = LocalExecutor(timeout=timeout)
+
+    if mode == 'local':
+        # start temporary rpc tracker and rpc server for the user
+        tracker = Tracker('localhost', port=9000, port_end=10000,
+                          silent=True)
+        rpc_device_key = '$local$device$%d' % tracker.port
+        server = Server('localhost', port=9000, port_end=10000,
+                        key=rpc_device_key,
+                        use_popen=True, silent=True,
+                        tracker_addr=(tracker.host, tracker.port))
+
+        fmeasure = measure_methods.measure_rpc
+        kwargs['rpc_device_key'] = rpc_device_key
+        kwargs['rpc_tracker_addr'] = (tracker.host, tracker.port)
+        kwargs['rpc_timeout'] = timeout
+        kwargs['tmp_dir'] = tempdir()
+    elif mode == 'rpc':
+        fmeasure = measure_methods.measure_rpc
+        kwargs['rpc_device_key'] = rpc_device_key
+        kwargs['rpc_priority'] = rpc_priority
+        kwargs['rpc_timeout'] = rpc_timeout
+        kwargs['use_ndk'] = use_ndk
+        kwargs['tmp_dir'] = tempdir()
+        assert rpc_device_key, "In rpc mode, a rpc_device_key must be provided"
+    elif mode == "custom":
+        assert callable(custom_measure_batch), "In custom mode, custom_measure_func " \
+                                               "must be a callable object"
+    elif mode == 'local-nofork':
+        fmeasure = measure_methods.measure_local
+        kwargs['fork_new_process'] = False
+    else:
+        raise RuntimeError("Invalid mode: " + mode)
+
+    if 'cuda' in task.target.keys and 'rpc_device_key' in kwargs:  # query cuda device info
+        add_cuda_device_info(kwargs['rpc_device_key'], kwargs.get('rpc_tracker_addr'), kwargs)
+    if 'opencl' in task.target.keys and 'rpc_device_key' in kwargs:
+        add_opencl_device_info(kwargs['rpc_device_key'], kwargs.get('rpc_tracker_addr'), kwargs)
+
+    if check_correctness:
+        # use llvm to generate a reference input/output
+        # this option works for tuning topi, but might not work for you custom op
+        with _target.create("llvm"):
+            s, arg_bufs = task.instantiate(task.config_space.get(0))
+        ref_input = [np.random.uniform(size=get_const_tuple(x.shape)).astype(x.dtype)
+                     for x in arg_bufs]
+        func = build(s, arg_bufs, "llvm")
+        tvm_buf = [nd.array(x) for x in ref_input]
+        func(*tvm_buf)
+        ref_output = [x.asnumpy() for x in tvm_buf]
+        kwargs['ref_input'], kwargs['ref_outpu'] = ref_input, ref_output
+
+    def measure_batch(measure_inputs):
+        """measure the time cost for a batch of configs in real machines"""
+        if replay_db is not None:
+            partial_results, measure_inputs =\
+                filter_inputs(replay_db, measure_inputs, retry=False)
+
+        # pack configs
+        input_packs = []
+        for i in range(0, len(measure_inputs), pack_size):
+            input_packs.append(measure_inputs[i:i + pack_size])
+
+        # send to measure
+        futures = []
+        for input_pack in input_packs:
+            future = executor.submit(
+                fmeasure, input_pack,
+                number=number,
+                repeat=repeat,
+                build_option=build_option,
+                **kwargs
+            )
+            futures.append(future)
+
+        # transform results
+        results = []
+        for future in futures:
+            result = future.get()
+            if isinstance(result, Exception):
+                if mode == 'local-nofork':
+                    # debug usage, raise exception
+                    raise result
+                tstamp = time.time()
+                results.extend([MeasureResult((result,), MeasureErrorNo.FLEET_ERROR,
+                                              timeout, tstamp)] * pack_size)
+            else:
+                results.extend(result)
+
+        if replay_db is not None:
+            if save_to_replay_db:  # save result to database
+                for measure_input, result in zip(measure_inputs, results):
+                    replay_db.save(measure_input, result)
+
+            result_idx = 0
+            for i in range(len(partial_results)):
+                if partial_results[i] is None:
+                    partial_results[i] = results[result_idx]
+                    result_idx += 1
+            return partial_results
+        return results
+
+    if mode == 'custom':
+        measure_batch = custom_measure_batch
+
+    measure_batch.parallel_num = parallel_num
+    if mode == 'local':
+        measure_batch.aux_objects = {"server": server, "tracker": tracker}
+    return measure_batch
+
+
+def add_cuda_device_info(device_key, rpc_tracker_addr, kwargs):
+    """Query cuda device info. This is used to set the flags for nvcc compiler
+    and check the validity of a generated code."""
+    from .measure_methods import request_remote
+
+    remote = request_remote(device_key, rpc_tracker_addr)
+    ctx = remote.context('cuda', 0)
+    max_dims = ctx.max_thread_dimensions
+    kwargs['check_gpu'] = {
+        'max_shared_memory_per_block': ctx.max_shared_memory_per_block,
+        'max_threads_per_block': ctx.max_threads_per_block,
+        'max_thread_x': max_dims[0],
+        'max_thread_y': max_dims[1],
+        'max_thread_z': max_dims[2],
+    }
+
+    kwargs["cuda_arch"] = "sm_" + "".join(ctx.compute_version.split('.'))
+
+def add_opencl_device_info(device_key, rpc_tracker_addr, kwargs):
+    """Query opencl device info. This is used to check the validity of a generated code."""
+    from .measure_methods import request_remote
+
+    remote = request_remote(device_key, rpc_tracker_addr)
+    ctx = remote.context('opencl', 0)
+    max_dims = ctx.max_thread_dimensions
+    kwargs['check_gpu'] = {
+        'max_shared_memory_per_block': ctx.max_shared_memory_per_block,
+        'max_threads_per_block': ctx.max_threads_per_block,
+        'max_thread_x': max_dims[0],
+        'max_thread_y': max_dims[1],
+        'max_thread_z': max_dims[2],
+    }

python/tvm/autotvm/measure/measure_methods.py

+# pylint: disable=consider-using-enumerate,invalid-name,too-many-function-args
+"""
+Functions that run on executor for measurement.
+These functions are responsible for building tvm module, uploading it to
+remote devices, recording the running time costs and checking the correctness of output
+"""
+
+import logging
+import os
+import time
+from random import getrandbits
+
+import numpy as np
+
+from ...contrib import ndk, nvcc
+from ... import rpc, ir_pass, build, build_config, nd, context, TVMError, register_func
+
+from ..util import get_const_tuple
+from ..env import AutotvmGlobalScope
+from .measure import MeasureResult, MeasureErrorNo
+from ..task.space import InstantiationError
+
+
+class HashMismatchError(ValueError):
+    """Raised when the code hash of a submitted config doesn't match that on the
+       measure side """
+    pass
+
+def request_remote(device_key, tracker_addr=None, priority=1, timeout=60):
+    """request a remote session
+
+    Parameters
+    ----------
+    device_key: string
+        device key of registered device in tracker
+    tracker_addr: Tuple(string, int), optional
+        The address of rpc tracker in (host, port) format
+    priority: int, optional
+        priority of this request, larger is more prior
+    timeout: float, optional
+        timeout of this session (units: seconds)
+
+    Returns
+    ------
+    session: RPCSession
+    """
+    # connect to the tracker
+    if tracker_addr:
+        host = tracker_addr[0]
+        port = tracker_addr[1]
+    else:
+        host = os.environ['TVM_TRACKER_HOST']
+        port = int(os.environ['TVM_TRACKER_PORT'])
+
+    tracker = rpc.connect_tracker(host, port)
+    remote = tracker.request(device_key, priority=priority,
+                             session_timeout=timeout)
+    return remote
+
+
+def _measure_generic(fbuild, input_pack, ref_input, ref_output):
+    """Generic measurement function
+
+    Parameters
+    ----------
+    fbuild : function takes MeasureInput returns tuple of (time_func, ctx)
+        The build function used to build each input.
+    input_pack : list of MeasureInput
+        The inputs we need to evaluate
+    ref_input: Array of np.ndarray
+        Reference input for checking correctness
+    ref_output: Array of np.ndarray
+        Reference output for checking correctness
+
+    Returns
+    -------
+    res_pack : array of MeasureResult
+        The list of execution result of measurement.
+    """
+    res_pack = []
+    for inp in input_pack:
+        tic = time.time()
+        try:
+            time_f, ctx, arg_bufs = fbuild(inp)
+        except TVMError as exc:
+            tstamp = time.time()
+            msg = str(exc)
+            if "Stack trace returned" in msg:
+                msg = msg[:msg.index("Stack trace returned")]
+            if "InstantiationError" in msg:
+                try:
+                    msg = msg.split('\n')[-2].split(": ")[1]
+                except Exception:  # pylint: disable=broad-except
+                    pass
+                res_pack.append(MeasureResult((InstantiationError(msg),),
+                                              MeasureErrorNo.INSTANTIATION_ERROR,
+                                              tstamp - tic, tstamp))
+            else:
+                res_pack.append(MeasureResult((RuntimeError(msg),),
+                                              MeasureErrorNo.COMPILE_HOST,
+                                              tstamp - tic, tstamp))
+            continue
+        except InstantiationError as e:
+            tstamp = time.time()
+            res_pack.append(MeasureResult((e,),
+                                          MeasureErrorNo.INSTANTIATION_ERROR,
+                                          tstamp - tic, tstamp))
+            continue
+
+        # measure time
+        errno = MeasureErrorNo.NO_ERROR
+        try:
+            if ref_input:
+                args = [nd.array(x, ctx) for x in ref_input]
+            else:
+                args = [nd.array(np.random.uniform(size=get_const_tuple(x.shape)).astype(x.dtype),
+                                 ctx) for x in arg_bufs]
+            costs = time_f(*args).results
+            if len(costs) > 2:  # remove largest and smallest value to reduce variance
+                costs = list(costs)
+                costs.sort()
+                costs = tuple(costs[1:-1])
+            if ref_output:
+                for expected, real in zip(ref_output, args):
+                    if not np.allclose(expected, real.asnumpy(), rtol=1e-4):
+                        logging.warning("Wrong Answer!")
+                        errno = MeasureErrorNo.WRONG_ANSWER
+        except TVMError as exc:
+            msg = str(exc)
+            if "Stack trace returned" in msg:
+                msg = msg[:msg.index("Stack trace returned")]
+            costs = (RuntimeError(msg),)
+            errno = MeasureErrorNo.RUNTIME_DEVICE
+        tstamp = time.time()
+        res_pack.append(MeasureResult(costs, errno, tstamp - tic, tstamp))
+    return res_pack
+
+def _build_func(inp, build_option, kwargs):
+    """Build function module. Exception will be raised when error occurs"""
+    with inp.target:
+        s, args = inp.task.instantiate(inp.config)
+        if not inp.config.valid():
+            raise InstantiationError(inp.config.errors)
+        code_hash = getattr(s, 'code_hash', None)
+        if inp.config.code_hash != code_hash:
+            raise HashMismatchError('got {0:s}, expected {1:s}'
+                                    .format(str(inp.config.code_hash), str(code_hash)))
+
+        opts = build_option or {}
+        if "check_gpu" in kwargs:
+            values = kwargs['check_gpu']
+            # Add gpu verify pass to filter out invalid configs in advance.
+            # This can accelerate the tuning process
+            check_keys = ['max_shared_memory_per_block', 'max_threads_per_block',
+                          'max_thread_x', 'max_thread_y', 'max_thread_z']
+            opts["add_lower_pass"] = [
+                (2, gpu_verify_pass(**{key: values[key] for key in check_keys}))]
+
+        if 'cuda_arch' in kwargs:
+            set_cuda_target_arch(kwargs['cuda_arch'])
+
+        with build_config(**opts):
+            func = build(s, args, target_host=inp.task.target_host)
+
+    return func, args
+
+
+def measure_rpc(input_pack,
+                rpc_device_key,
+                number,
+                repeat=1,
+                build_option=None,
+                rpc_tracker_addr=None,
+                rpc_priority=1,
+                rpc_timeout=60,
+                tmp_dir=None,
+                **kwargs):
+    """Measure the time cost on a device by rpc
+
+    Parameters
+    ----------
+    input_pack : list of MeasureInput
+        The inputs we need to evaluate
+    rpc_device_key: str
+        The device key of registered devices in tracker
+    number : int
+        Number of times to get the running measurement
+    repeat : int, optional
+        How many times we want to repeat the measurement.
+    build_option: Dict
+        build options for tvm.build_config
+
+    rpc_tracker_addr: Tuple(string, int), optional
+        The address of rpc tracker in (host, port) format
+        If is none, will use environment variable
+    rpc_priority: int, optional
+        priority of this task, used by scheduler in tracker
+    rpc_timeout: int, optional
+        timeout of the rpc session
+
+    tmp_dir: tvm.contrib.util.TempDirectory, optional
+        directory to store temp file
+
+    kwargs: dict, optional
+        Additional key word arguments
+
+    Returns
+    -------
+    res_pack : Array of MeasureResult
+        The list of execution results of measurement.
+    """
+    def _fbuild(inp):
+        """ Local build function."""
+        func, args = _build_func(inp, build_option, kwargs)
+
+        if not kwargs.get('use_ndk', False):
+            file_name = "tmp_func_%0x.tar" % getrandbits(64)
+            path = tmp_dir.relpath(file_name)
+            func.export_library(path)
+        else:
+            file_name = "tmp_func_%0x.so" % getrandbits(64)
+            path = tmp_dir.relpath(file_name)
+            func.export_library(path, ndk.create_shared)
+        remote = request_remote(rpc_device_key, rpc_tracker_addr, rpc_priority, rpc_timeout)
+        remote.upload(path)
+        func = remote.load_module(file_name)
+        ctx = remote.context(str(inp.target), 0)
+        time_f = func.time_evaluator(
+            func.entry_name, ctx, number=number, repeat=repeat)
+        return time_f, ctx, args
+
+    ret = _measure_generic(_fbuild, input_pack,
+                           kwargs.get("ref_input", None), kwargs.get("ref_output", None))
+    return ret
+
+
+def measure_local(input_pack,
+                  number,
+                  repeat=1,
+                  build_option=None,
+                  **kwargs):
+    """Measure the time cost on a local machine.
+
+    Parameters
+    ----------
+    input_pack : list of MeasureInput
+        The inputs we need to evaluate
+    number : int
+        Number of times to get the running measurement
+    repeat : int, optional
+        How many times we want to repeat the measurement.
+    build_option: dict, optional
+        Build options for tvm.build_config
+    kwargs: dict, optional
+        Additional key word arguments
+
+    Returns
+    -------
+    res_pack : Array of MeasureResult
+        The list of execution results of measurement.
+    """
+
+    def _fbuild(inp):
+        """ Local build function """
+        func, args = _build_func(inp, build_option, kwargs)
+        ctx = context(str(inp.target), 0)
+        time_f = func.time_evaluator(
+            func.entry_name, ctx, number=number, repeat=repeat)
+        return time_f, ctx, args
+
+    ret = _measure_generic(_fbuild, input_pack,
+                           kwargs.get("ref_input", None), kwargs.get("ref_output", None))
+    return ret
+
+
+def gpu_verify_pass(**kwargs):
+    """Verify the validity of a gpu kernel
+    This pass will check shared memory size and number of threads per block.
+    """
+    def verify_pass(stmt):
+        valid = ir_pass.VerifyGPUCode(stmt, kwargs)
+        if not valid:
+            raise InstantiationError("Skipped because of invalid gpu kernel")
+        return stmt
+    return verify_pass
+
+
+@register_func
+def tvm_callback_cuda_compile(code):
+    """use nvcc to generate ptx code for better optimization"""
+    ptx = nvcc.compile_cuda(code, target="ptx", arch=AutotvmGlobalScope.current.cuda_target_arch)
+    return ptx
+
+def set_cuda_target_arch(arch):
+    """set target architecture of nvcc compiler"""
+    AutotvmGlobalScope.current.cuda_target_arch = arch

python/tvm/autotvm/record.py

+# pylint: disable=superfluous-parens, redefined-outer-name, redefined-outer-name,pointless-string-statement
+# pylint: disable=consider-using-enumerate,invalid-name
+"""Tuning record and serialization format"""
+
+import argparse
+import base64
+import logging
+import multiprocessing
+import pickle
+import json
+import time
+from collections import OrderedDict
+
+import numpy as np
+
+from .. import target, build, lower
+
+from . import task
+from .task import DispatchContext, ConfigEntity
+from .measure import MeasureInput, MeasureResult
+
+AUTOTVM_LOG_VERSION = 0.1
+
+try:  # convert unicode to str for python2
+    _unicode = unicode
+except NameError:
+    _unicode = ()
+
+
+def measure_str_key(inp, include_config=True):
+    """ get unique str key for MeasureInput
+
+    Parameters
+    ----------
+    inp: MeasureInput
+        input for the measure
+    include_config: bool, optional
+        whether includes config in the str key
+
+    Returns
+    -------
+    key: str
+        The str representation of key
+    """
+    config_str = str(inp.config) if include_config else ""
+    return "".join([str(inp.target), inp.task.name, str(inp.task.args),
+                    str(inp.task.kwargs), config_str])
+
+
+def encode(inp, result, protocol='json'):
+    """encode (MeasureInput, MeasureResult) pair to a string
+
+    Parameters
+    ----------
+    inp: autotvm.tuner.MeasureInput
+    result: autotvm.tuner.MeasureResult
+        pair of input/result
+    protocol: str
+        log protocol, json or pickle
+
+    Returns
+    -------
+    row: str
+        a row in the logger file
+    """
+
+    if protocol == 'json':
+        json_dict = {
+            "i": (str(inp.target),
+                  inp.task.name, inp.task.args, inp.task.kwargs,
+                  inp.task.workload,
+                  inp.config.to_json_dict()),
+
+            "r": (result.costs if result.error_no == 0 else (1e9,),
+                  result.error_no,
+                  result.all_cost,
+                  result.timestamp),
+
+            "v": AUTOTVM_LOG_VERSION
+        }
+        return json.dumps(json_dict)
+    elif protocol == 'pickle':
+        row = (str(inp.target),
+               str(base64.b64encode(pickle.dumps([inp.task.name,
+                                                  inp.task.args,
+                                                  inp.task.kwargs,
+                                                  inp.task.workload])).decode()),
+               str(base64.b64encode(pickle.dumps(inp.config)).decode()),
+               str(base64.b64encode(pickle.dumps(tuple(result))).decode()))
+        return '\t'.join(row)
+    else:
+        raise RuntimeError("Invalid log protocol: " + protocol)
+
+
+def decode(row, protocol='json'):
+    """Decode encoded record string to python object
+
+    Parameters
+    ----------
+    row: str
+        a row in the logger file
+    protocol: str
+        log protocol, json or pickle
+
+    Returns
+    -------
+    input: autotvm.tuner.MeasureInput
+    result: autotvm.tuner.MeasureResult
+    """
+    # pylint: disable=unused-variable
+    if protocol == 'json':
+        row = json.loads(row)
+        tgt, task_name, task_args, task_kwargs, workload, config = row['i']
+        tgt = target.create(str(tgt))
+
+        def clean_json_to_python(x):
+            """1. convert all list in x to tuple (hashable)
+               2. convert unicode to str for python2
+            """
+            if isinstance(x, list):
+                return tuple([clean_json_to_python(a) for a in x])
+            if isinstance(x, _unicode):
+                return str(x)
+            return x
+
+        tsk = task.Task(clean_json_to_python(task_name), clean_json_to_python(task_args))
+        tsk.workload = clean_json_to_python(workload)
+        config = ConfigEntity.from_json_dict(config)
+        inp = MeasureInput(tgt, tsk, config)
+        result = MeasureResult(*[tuple(x) if isinstance(x, list) else x for x in row["r"]])
+
+        return inp, result
+    elif protocol == 'pickle':
+        items = row.split("\t")
+        tgt = target.create(items[0])
+        task_tuple = pickle.loads(base64.b64decode(items[1].encode()))
+        config = pickle.loads(base64.b64decode(items[2].encode()))
+        result = pickle.loads(base64.b64decode(items[3].encode()))
+
+        tsk = task.Task(task_tuple[0], task_tuple[1])
+        tsk.workload = task_tuple[3]
+        return MeasureInput(tgt, tsk, config), MeasureResult(*result)
+    else:
+        raise RuntimeError("Invalid log protocol: " + protocol)
+
+def load_from_file(filename):
+    """Generator: load records from file.
+    This is a generator that yields the records.
+
+    Parameters
+    ----------
+    filename: str
+
+    Yields
+    ------
+    input: autotvm.tuner.MeasureInput
+    result: autotvm.tuner.MeasureResult
+    """
+    for row in open(filename):
+        yield decode(row)
+
+
+class ApplyHistoryBest(DispatchContext):
+    """
+    Apply the history best config
+
+    Parameters
+    ----------
+    records : str or iterator of (MeasureInput, MeasureResult)
+        Collection of tuning records.
+        if is str, then it should be the filename of a records log file.
+                   Each row of this file is an encoded record pair.
+        otherwise, it is an iterator
+    default: ConfigEntity, optional
+        default config to return when no history records
+    """
+    def __init__(self, records, default=None):
+        super(ApplyHistoryBest, self).__init__()
+
+        if isinstance(records, str):
+            records = load_from_file(records)
+
+        counter = 0
+        best_map = {}
+        for inp, res in records:
+            counter += 1
+            if res.error_no != 0:
+                continue
+            for k in inp.target.keys:
+                key = (k, inp.task.workload)
+                if key not in best_map:
+                    best_map[key] = (inp, res)
+                else:
+                    _, other_res = best_map[key]
+                    if np.mean(other_res.costs) > np.mean(res.costs):
+                        best_map[key] = (inp, res)
+        logging.info(
+            "Finish load %d records, %d entries selected", counter, len(best_map))
+        self._best_map = best_map
+        self._default = default
+
+    def query(self, target, workload):
+        if target is None:
+            raise RuntimeError("Need a target context to find the history best. "
+                               "Hint: If your target is llvm, use `with tvm.target.create('llvm'):`"
+                               " above the dispatcher call. So does other target. ")
+
+        for k in target.keys:
+            key = (k, workload)
+            if key in self._best_map:
+                return self._best_map[key][0].config
+
+        if self._default:
+            return self._default
+        raise RuntimeError(
+            "Cannot find config for target=%s, workload=%s" % (target, workload))
+
+    def dump_best(self, out_file):
+        """Dump the best records for each workload to a file
+
+        Parameters
+        ----------
+        out_file: str
+            filename
+        """
+        fout = open(out_file, 'a')
+        for val in self._best_map.values():
+            inp, res = val
+            fout.write(encode(inp, res) + '\n')
+
+
+def split_workload(in_file, clean=True):
+    """Split a log file into separate files, each of which contains only a single workload
+    This function can also delete duplicated records in log file
+
+    Parameters
+    ----------
+    in_file: str
+        input filename
+    clean: bool
+        whether delete duplicated items
+    """
+    tic = time.time()
+    lines = list(open(in_file).readlines())
+
+    logging.info("start convert...")
+    pool = multiprocessing.Pool()
+    lines = pool.map(decode, lines)
+    logging.info("map done %.2f", time.time() - tic)
+
+    wkl_dict = OrderedDict()
+    for inp, res in lines:
+        wkl = measure_str_key(inp, False)
+        if wkl not in wkl_dict:
+            wkl_dict[wkl] = []
+        wkl_dict[wkl].append([inp, res])
+
+    if clean:
+        for i, (k, v) in enumerate(wkl_dict.items()):
+            # clean duplicated items
+            added = set()
+            cleaned = []
+            for inp, res in v:
+                str_key = measure_str_key(inp)
+                if str_key in added:
+                    continue
+                added.add(str_key)
+                cleaned.append([inp, res])
+
+            # write to file
+            logging.info("Key: %s\tValid: %d\tDup: %d\t", k, len(cleaned), len(v) - len(cleaned))
+            with open(args.i + ".%03d.wkl" % i, 'w') as fout:
+                for inp, res in cleaned:
+                    fout.write(encode(inp, res) + '\n')
+    else:
+        for i, (k, v) in enumerate(wkl_dict.items()):
+            logging.info("Key: %s\tNum: %d", k, len(v))
+            with open(args.i + ".%03d.wkl" % i, 'w') as fout:
+                for inp, res in v:
+                    fout.write(encode(inp, res) + '\n')
+
+
+"""
+Usage:
+This record executable module has three modes.
+
+* Print log file in readable format
+e.g. python -m autotvm.record --mode read --i collect_conv.tsv --begin 0 --end 5 --ir --code
+
+* Extract history best from a large log file
+e.g. python -m autotvm.record --mode best --i collect.tsv
+
+* Split a log file into separate files, each of which contains only a single wkl
+e.g. python -m autotvm.record --mode split --i collect.tsv
+"""
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--mode", choices=['read', 'best', 'split'], default='read')
+    parser.add_argument("--i", type=str, help="input file")
+    parser.add_argument("--o", type=str, default=None, help='output file')
+    parser.add_argument("--begin", type=int, default=0)
+    parser.add_argument("--end", type=int, default=5)
+    parser.add_argument("--ir", action='store_true')
+    parser.add_argument("--code", action='store_true')
+
+    args = parser.parse_args()
+    logging.basicConfig(level=logging.INFO)
+
+    if args.mode == 'best':
+        args.o = args.o or args.i + ".best"
+        hist_best = ApplyHistoryBest(load_from_file(args.i))
+        hist_best.dump_best(args.o)
+    elif args.mode == 'read':
+        for i, (inp, result) in enumerate(load_from_file(args.i)):
+            if args.begin <= i < args.end:
+                with inp.target:
+                    s, arg_bufs = inp.task.instantiate(inp.config)
+
+                print("")
+                print(inp.target, inp.task, inp.config)
+                print(result)
+
+                if args.ir:
+                    with inp.target:
+                        print(lower(s, arg_bufs, simple_mode=True))
+
+                if args.code:
+                    with inp.target:
+                        func = build(s, arg_bufs)
+                        print(func.imported_modules[0].get_source())
+    elif args.mode == 'split':
+        split_workload(args.i)

python/tvm/autotvm/task/__init__.py

+"""Task is a tunable composition of template functions.
+
+Tuner takes a tunable task and optimizes the joint configuration
+space of all the template functions in the task.
+This module defines the task data structure, as well as a collection(zoo)
+of typical tasks of interest.
+"""
+
+from .task import Task, create, register, template, get_config
+from .space import ConfigSpace, ConfigEntity
+from .code_hash import attach_code_hash, attach_code_hash_to_arg
+from .dispatcher import DispatchContext, ApplyConfig, dispatcher

python/tvm/autotvm/task/code_hash.py

+"""
+Decorator functions for hashing schedule code
+
+code hashing is used to check the consistence of schedule code and the parameters loaded from log
+"""
+import inspect
+import zlib
+
+from tvm import schedule
+
+def attach_code_hash(s):
+    """Decorator for attaching a code hash to a schedule
+
+    Parameters
+    ----------
+    s: Schedule
+        tvm.schedule.Schedule to attach the hash to
+    """
+    def decorator(func):
+        def wrapper(*args, **kwargs):
+            func(*args, **kwargs)
+            raw_hash = zlib.crc32(''.join(inspect.getsourcelines(func)[0]).encode())
+            s.code_hash = hex(raw_hash)[2:]
+        return wrapper
+    return decorator
+
+def attach_code_hash_to_arg(arg_idx=1):
+    """Decorator for attaching a code hash to a schedule
+
+    Parameters
+    ----------
+    arg_idx: int
+        index of the argument (expected to be a Schedule) to attach the code
+        hash to
+    """
+    def decorator(func):
+        def wrapper(*args, **kwargs):
+            func(*args, **kwargs)
+            assert isinstance(args[arg_idx], schedule.Schedule)
+            raw_hash = zlib.crc32(''.join(inspect.getsourcelines(func)[0]).encode())
+            args[arg_idx].code_hash = hex(raw_hash)[2:]
+        return wrapper
+    return decorator

python/tvm/autotvm/task/dispatcher.py

+"""
+Template dispatcher module.
+
+A dispatcher is a function that can contains multiple behaviors.
+Its specific behavior is can be controlled by DispatchContext.
+
+DispatchContext is used in two ways, usually via different implementation
+of the DispatchContext base class.
+
+- During search, we can use it to pass the current proposal from tuner.
+- During evaluation, we can use it to set pick the best policy.
+"""
+from __future__ import absolute_import as _abs
+
+from decorator import decorate
+
+from tvm import target as _target
+
+class DispatchContext(object):
+    """
+    Base class of dispatch context.
+
+    DispatchContext enables the target and workload
+    specific dispatch mechanism for templates.
+    """
+    current = None
+
+    def query(self, target, workload):
+        """
+        Query the context to get the specific implementation.
+
+        Parameters
+        ----------
+        target: Target
+            The current target
+        workload : Workload
+            The current workload.
+
+        Returns
+        -------
+        cfg : ConfigSpace
+            The specific configuration.
+        """
+        raise NotImplementedError()
+
+    def __enter__(self):
+        self._old_ctx = DispatchContext.current
+        DispatchContext.current = self
+        return self
+
+    def __exit__(self, ptype, value, trace):
+        DispatchContext.current = self._old_ctx
+
+
+class ApplyConfig(DispatchContext):
+    """Apply a specific config entity during query.
+
+    Parameters
+    ----------
+    config : ConfigSpace or ConfigEntity
+        The specific configuration we care about.
+    """
+    def __init__(self, config):
+        super(ApplyConfig, self).__init__()
+        self._config = config
+        self.workload = None
+
+    def query(self, target, workload):
+        """Override query"""
+        self.workload = workload
+        return self._config
+
+
+def dispatcher(fworkload):
+    """Wrap a workload dispatcher function.
+
+    Parameters
+    ----------
+    fworkload : function
+        The workload extraction function from arguments.
+
+    Returns
+    -------
+    fdispatcher : function
+        A wrapped dispatcher function, which will
+        dispatch based on DispatchContext and
+        the current workload.
+    """
+    dispatch_dict = {}
+    func_name = fworkload.__name__
+
+    def register(key, func=None, override=False):
+        """Register template function.
+
+        Parameters
+        ----------
+        key : str or List of str
+            The template key to identify the template
+            under this dispatcher.
+        func : function
+            The function to be registered.
+            The first argument of the function is always
+            cfg returned by DispatchContext,
+            the rest arguments are the same as the fworkload.
+        override : bool
+            Whether override existing registration.
+
+        Returns
+        -------
+        The register function if necessary.
+        """
+        if isinstance(key, str):
+            key = [key]
+
+        def _do_reg(myf):
+            for x in key:
+                if x in dispatch_dict and not override:
+                    raise ValueError(
+                        "Key %s is already registered for %s" % (x, func_name))
+                dispatch_dict[x] = myf
+            return myf
+
+        if func:
+            return _do_reg(func)
+        return _do_reg
+
+    def dispatch_func(func, *args, **kwargs):
+        """The wrapped dispatch function"""
+        tgt = _target.current_target()
+        context = DispatchContext.current
+        if context is None:
+            raise RuntimeError("DispatchContext is not initialized")
+        workload = func(*args, **kwargs)
+        cfg = context.query(tgt, workload)
+        return dispatch_dict[cfg.template_key](cfg, *args, **kwargs)
+
+    fdecorate = decorate(fworkload, dispatch_func)
+    fdecorate.register = register
+    return fdecorate

python/tvm/autotvm/task/space.py

+# pylint: disable=too-few-public-methods,invalid-name,unused-argument,arguments-differ
+# pylint: disable=consider-using-enumerate
+"""
+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'])
+
+
+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
+    """
+    pass
+
+
+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):
+            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")
+
+    @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
+
+
+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 = []
+
+        if policy == 'all':
+            num_outputs = kwargs["num_outputs"]
+            max_factor = kwargs.get("max_factor", 1 << 31)
+            fil = kwargs.get("filter", lambda x: True)
+
+            length = axis.length
+            factors = get_factors(length)
+            factors = [x for x in factors if x <= max_factor]
+            # copy factors for every level
+            self.product = length
+            self.num_outputs = num_outputs
+            self.factors = [factors] * (num_outputs-1)
+            self._generate_space(0, [None] * (num_outputs - 1))
+            self.entities = list(filter(fil, self.entities))
+            self.num_output = num_outputs
+        elif policy == 'candidate':
+            self.product = axis.length
+            self.num_outputs = kwargs["num_outputs"]
+            for size in kwargs["candidate"]:
+                assert len(size) == self.num_outputs
+                # assert np.prod(size) == self.product
+                self.entities.append(SplitEntity(size))
+            self.num_output = self.num_outputs
+        else:
+            raise RuntimeError("Invalid policy: " + policy)
+
+    def _generate_space(self, now, tmp_stack):
+        """Generate space by DFS"""
+        if now == self.num_outputs - 1:
+            if self.product % np.prod(tmp_stack) == 0:
+                first = int(self.product // int(np.prod(tmp_stack)))
+                self.entities.append(SplitEntity([first] + tmp_stack[::-1]))
+        else:
+            for factor in self.factors[now]:
+                tmp_stack[now] = factor
+                self._generate_space(now + 1, tmp_stack)
+
+    @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_outputs, 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
+        else:
+            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(np.arange(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 == 0 or vec_ct == 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()
+        self._constraints = []
+        self.errors = []
+        self.template_key = None
+        self.code_hash = None
+        self.flop = 0
+
+    @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='all', **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 'all', the tuner will try all divisible factors.
+            If is 'candidate', try listed candidate.
+        kwargs: dict
+            extra arguments for policy
+        """
+        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)

python/tvm/autotvm/task/task.py

+# pylint: disable=unused-variable
+"""Definition of task function.
+
+Task can be constructed from tuple of func, args, and kwargs.
+func is a state-less function, or a string that
+registers the standard task.
+"""
+
+import numpy as np
+
+from ... import tensor, expr, container, target as _target
+
+from ..util import get_const_int, get_const_tuple, get_func_name
+from .dispatcher import DispatchContext, ApplyConfig, dispatcher
+from .space import ConfigSpace
+
+def _raise_error(*args, **kwargs):  # pylint: disable=unused-argument
+    raise RuntimeError("The function of this task is not found. Possibly the function "
+                       "of this task is registered in another python file "
+                       "which is not imported in this run")
+
+class Task(object):
+    """A Tunable Task
+
+    Parameters
+    ----------
+    name: str
+        The name of the task.
+    args: Tuple
+        Positional argument of func
+    """
+    def __init__(self, name, args):
+        self.name = name
+        self.args = args
+        self.kwargs = {}  # currently unused
+
+        # init null config space
+        self.config_space = None
+        self.func = TASK_TABLE.get(name, _raise_error)
+
+        # auxiliary info, available after `init_space` is called
+        self.workload = None
+        self.flop = None
+        self.target = None
+        self.target_host = None
+
+    def instantiate(self, config):
+        """Instantiate this task function (template) with a config.
+        Returns corresponding schedule.
+
+        Parameters
+        ----------
+        config: template.ConfigEntity
+            parameter config for this template
+
+        Returns
+        -------
+        sch: tvm.schedule.Schedule
+            The tvm schedule
+        arg_bufs: Array of tvm.tensor.Tensor
+            The input/output buffers
+        """
+        config.flop = 0
+        with ApplyConfig(config):
+            sch, arg_bufs = self.func(*self.args, **self.kwargs)
+        if not self.flop:
+            config.flop = config.flop or compute_flop(sch)
+            self.flop = config.flop
+        return sch, arg_bufs
+
+    def __repr__(self):
+        return "Task(func_name=%s, args=%s, kwargs=%s, workload=%s)" % (
+            self.name, self.args, self.kwargs, self.workload
+        )
+
+TASK_TABLE = {
+}
+
+def register(name, func=None, override=False):
+    """Register a task function.
+
+    Parameters
+    ----------
+    name : str
+        The name to identify the task.
+    func : callable
+        The function to be registered.
+    override : bool
+        Whether override existing registration.
+
+    Returns
+    -------
+    func: callable
+        The registered function
+    """
+    def _do_reg(myf):
+        if name in TASK_TABLE and not override:
+            raise ValueError(
+                "Key %s is already registered" % name)
+        TASK_TABLE[name] = myf
+        return myf
+    if func:
+        return _do_reg(func)
+    return _do_reg
+
+def create(func_name, args, target, target_host=None, template_key=None):
+    """Create a tuning task and initialize its search space
+
+    Parameters
+    ----------
+    func_name : str or callable
+        The task function
+    args : List
+        Positional arguments
+    target : Target
+        The compilation target
+    target_host: Target, optional
+        The compilation target for host side
+
+    Returns
+    -------
+    tsk: Task
+        a task object
+    """
+    if callable(func_name):
+        # register this function if it is not registered before
+        func = func_name
+        func_name = func.func_name if hasattr(func, 'func_name') else func.__name__
+        if func_name in TASK_TABLE:
+            assert func == TASK_TABLE[func_name], "Find name conflict in task registration. " \
+                                                  "Consider to choose another name for this task"
+        else:
+            register(func_name, func=func)
+
+    func = TASK_TABLE[func_name]
+    ret = Task(func_name, args)
+
+    if isinstance(target, str):
+        target = _target.create(target)
+
+    # init config space
+    ret.config_space = ConfigSpace()
+    ret.config_space.template_key = template_key or ""
+
+    ctx = ApplyConfig(ret.config_space)
+    with ctx:
+        with target:
+            sch, _ = func(*args)
+            ret.config_space.code_hash = getattr(sch, 'code_hash', None)
+
+    ret.workload = ctx.workload
+    ret.flop = ret.config_space.flop or compute_flop(sch)
+    ret.target = target
+    ret.target_host = target_host
+
+    return ret
+
+def args_to_workload(x):
+    """Convert argument list to hashable workload tuple.
+    This function will convert list to tuple, tvm node to python value and
+    flatten tvm.tensor.Tensor to a tuple
+
+    Parameters
+    ----------
+    x: primitive hashable types or tensor.Tensor
+        The original value
+
+    Returns
+    -------
+    ret: hashable
+        The hashable value
+    """
+    if isinstance(x, tensor.Tensor):
+        return get_const_tuple(x.shape) + (x.dtype, )
+    elif isinstance(x, (tuple, list, container.Array)):
+        return tuple([args_to_workload(a) for a in x])
+    elif isinstance(x, (str, int, float, np.int, np.float)):
+        return x
+    elif isinstance(x, (expr.StringImm, expr.IntImm, expr.FloatImm)):
+        return x.value
+    elif x is None:
+        return None
+    else:
+        raise RuntimeError('Do not support type "%s" in argument. Consider to use'
+                           'primitive types only' % type(x))
+
+def template(func):
+    """
+    Decorate a function as a tunable schedule template
+
+    Parameters
+    ----------
+    func: callable
+        A callable template function.
+        Its argument should be hashable values.
+        Its return value should be a Tuple(Schedule, Array of Tensor)
+
+    Returns
+    -------
+    func: callable
+        The decorated function
+
+    Examples
+    --------
+    The following code is a tunable template for a blocked matrix multiplication
+
+    .. code-block:: python
+
+        @autotvm.template
+        def matmul(N, L, M, dtype):
+            A = tvm.placeholder((N, L), name='A', dtype=dtype)
+            B = tvm.placeholder((L, M), name='B', dtype=dtype)
+
+            k = tvm.reduce_axis((0, L), name='k')
+            C = tvm.compute((N, M), lambda i, j: tvm.sum(A[i, k] * B[k, j], axis=k), name='C')
+            s = tvm.create_schedule(C.op)
+
+            # schedule
+            y, x = s[C].op.axis
+            k = s[C].op.reduce_axis[0]
+
+            ##### define space begin #####
+            cfg = autotvm.get_config()
+            cfg.define_split("tile_y", y, num_outputs=2)
+            cfg.define_split("tile_x", x, num_outputs=2)
+            ##### define space end #####
+
+            # schedule according to config
+            yo, yi = cfg["tile_y"].apply(s, C, y)
+            xo, xi = cfg["tile_x"].apply(s, C, x)
+
+            s[C].reorder(yo, xo, k, yi, xi)
+
+            return s, [A, B, C]
+    """
+    # pylint: disable=unused-variable
+
+    fname = get_func_name(func)
+
+    @register(fname)
+    @dispatcher
+    def config_dispatcher(*args, **kwargs):
+        assert not kwargs, "Do not support kwargs in template function call"
+        return (fname, ) + args_to_workload(args)
+
+    @config_dispatcher.register("")
+    def template_call(cfg, *args, **kwargs):
+        assert not kwargs, "Do not support kwargs in template function call"
+        with ApplyConfig(cfg):
+            return func(*args, **kwargs)
+
+    config_dispatcher.func_name = fname
+    return config_dispatcher
+
+def get_config():
+    """Get current config object
+
+    Returns
+    -------
+    cfg: ConfigSpace or ConfigEntity
+        The current config
+    """
+    return DispatchContext.current.query(None, None)
+
+class FlopCalculationError(RuntimeError):
+    """Error happens when estimating FLOP for a compute op"""
+    pass
+
+def compute_flop(sch):
+    """Calculate number of FLOP (floating number operations) of the compute ops in a schedule
+
+    Parameters
+    ----------
+    sch: tvm.schedule.Schedule
+        schedule
+
+    Returns
+    -------
+    flop: int
+        number of FLOP in this schedule
+    """
+    def _prod_length(axes):
+        """compute product of the lengths of a list of axes"""
+        try:
+            num_iter = int(np.prod([get_const_int(axis.dom.extent) for axis in axes]))
+        except ValueError:
+            raise FlopCalculationError("The length of axis is not constant. ")
+        return num_iter
+
+    def _count_flop(exp):
+        """compute flop for a single expression"""
+        if isinstance(exp, expr.Reduce):
+            num_iter = _prod_length(exp.axis)
+            combiner = exp.combiner.result
+            source = exp.source
+            if len(combiner) != 1:
+                raise FlopCalculationError("Found multiple output in the combiner of reduce op")
+            if len(source) != 1:
+                raise FlopCalculationError("Found multiple output in the source of reduce op")
+            return num_iter * (_count_flop(combiner[0]) + _count_flop(source[0]))
+        elif isinstance(exp, (expr.FloatImm, expr.IntImm, expr.UIntImm)):
+            return 0
+        elif isinstance(exp, expr.Cast):
+            return _count_flop(exp.value)
+        elif isinstance(exp, expr.Var):
+            return 0
+        elif isinstance(exp, (expr.Add, expr.Sub, expr.Mul, expr.Div, expr.Mod,
+                              expr.Max, expr.Min,
+                              expr.EQ, expr.NE, expr.LT, expr.LE, expr.GT, expr.GE,
+                              expr.And, expr.Or, expr.Not)):
+            base = 1 if "float" in exp.a.dtype else 0
+
+            if isinstance(exp, expr.Not):  # unary
+                return base + _count_flop(exp.a)
+
+            return base + _count_flop(exp.a) + _count_flop(exp.b)
+        elif isinstance(exp, expr.Select):
+            return _count_flop(exp.condition) + max(_count_flop(exp.true_value),
+                                                    _count_flop(exp.false_value))
+        elif isinstance(exp, expr.Call):
+            return sum([_count_flop(x) for x in exp.args])
+        else:
+            raise FlopCalculationError("Found unsupported operator in the compute expr")
+
+    def traverse(ops):
+        """accumulate flops"""
+        ret = 0
+        for op in ops:
+            if isinstance(op, tensor.ComputeOp):
+                num_element = _prod_length(op.axis)
+
+                body = op.body
+                if len(body) != 1:
+                    raise FlopCalculationError("Found multiple output in the compute")
+                exp = body[0]
+
+                ret += num_element * _count_flop(exp)
+                ret += traverse([sch[t].op for t in op.input_tensors])
+
+            elif isinstance(op, tensor.PlaceholderOp):
+                pass
+            else:
+                raise FlopCalculationError("Only support tvm.compute currently. "
+                                           "Other ops like tvm.scan is not supported")
+        return ret
+
+    try:
+        ret = traverse(sch.outputs)
+    except FlopCalculationError as exc:
+        raise RuntimeError("FLOP estimator fails for this operator. Error msg: "
+                           + str(exc) + ". Please use `cfg.add_flop` to manually set "
+                                        "FLOP for this operator")
+
+    if ret == 0:
+        raise RuntimeError("Cannot find float number operation in this operator. "
+                           "Please use `cfg.add_flop` to manually set "
+                           "FLOP for this operator")
+
+    return ret

python/tvm/autotvm/tuner/__init__.py

+"""
+A tuner takes a task as input. It proposes some promising :any:`ConfigEntity`
+in the :any:`ConfigSpace` and measure them on the real hardware. Then it
+proposed the next batch of :any:`ConfigEntity` according to the measure results.
+This tuning loop is repeated.
+"""
+
+from . import callback
+
+from .tuner import Tuner
+
+from .gridsearch_tuner import GridSearchTuner, RandomTuner
+from .ga_tuner import GATuner
+from .xgboost_tuner import XGBTuner

python/tvm/autotvm/tuner/callback.py

+# pylint: disable=consider-using-enumerate,invalid-name
+"""Namespace of callback utilities of AutoTVM"""
+
+import numpy as np
+
+from .. import record
+
+def log_to_file(file_out, protocol='json'):
+    """Log the tuning records into file.
+    The rows of the log are stored in the format of autotvm.record.encode.
+
+    Parameters
+    ----------
+    file_out : File or str
+        The file to log to.
+    protocol: str, optional
+        The log protocol. Can be 'json' or 'pickle'
+
+    Returns
+    -------
+    callback : callable
+        Callback function to do the logging.
+    """
+
+    def _callback(_, inputs, results):
+        """Callback implementation"""
+        if isinstance(file_out, str):
+            with open(file_out, "a") as f:
+                for inp, result in zip(inputs, results):
+                    f.write(record.encode(inp, result, protocol) + "\n")
+        else:
+            for inp, result in zip(inputs, results):
+                file_out.write(record.encode(inp, result, protocol) + "\n")
+    return _callback
+
+
+def save_tuner_state(prefix, save_every_sample=100):
+    """Save the state of tuner
+
+    Parameters
+    ----------
+    prefix : srt
+        prefix of the filename to store state
+    save_every_sample: int
+        save the state every x samples
+
+    Returns
+    -------
+    callback : function
+        Callback function to do the auto saving.
+    """
+    def _callback(tuner, inputs, results):
+        for _, __ in zip(inputs, results):
+            try:
+                ct = len(tuner.visited)
+            except AttributeError:
+                ct = 0
+            if ct % save_every_sample == 0:
+                tuner.save_state(prefix + "_%d.state" % ct)
+
+    return _callback
+
+
+def log_to_redis(host="localhost", port=6379, dbn=11):
+    """Record the tuning record to a redis DB.
+
+    Parameters
+    ----------
+    host: str, optional
+        Host address of redis db
+    port: int, optional
+        Port of redis db
+    dbn: int, optional
+        which redis db to use, default 11
+    """
+    # import here so only depend on redis when necessary
+    import redis
+    red = redis.StrictRedis(host=host, port=port, db=dbn)
+
+    def _callback(_, inputs, results):
+        """Callback implementation"""
+        for inp, result in zip(inputs, results):
+            red.set(inp, result)
+    return _callback
+
+class Monitor(object):
+    """A monitor to collect statistic during tuning"""
+    def __init__(self):
+        self.scores = []
+        self.timestamps = []
+
+    def __call__(self, tuner, inputs, results):
+        for inp, res in zip(inputs, results):
+            if res.error_no == 0:
+                flops = inp.task.flop / np.mean(res.costs)
+                self.scores.append(flops)
+            else:
+                self.scores.append(0)
+
+            self.timestamps.append(res.timestamp)
+
+    def reset(self):
+        self.scores = []
+        self.timestamps = []
+
+    def trial_scores(self):
+        """get scores (currently is flops) of all trials"""
+        return np.array(self.scores)
+
+    def trial_timestamps(self):
+        """get wall clock time stamp of all trials"""
+        return np.array(self.timestamps)

python/tvm/autotvm/tuner/ga_tuner.py

+# pylint: disable=consider-using-enumerate,invalid-name,abstract-method
+
+"""Tuner with genetic algorithm"""
+
+import numpy as np
+
+from .tuner import Tuner
+from .model_based_tuner import knob2point, point2knob
+
+
+class GATuner(Tuner):
+    """Tuner with genetic algorithm.
+    This tuner does not have a cost model so it always run measurement on real machines.
+    This tuner expands the :code:`ConfigEntity` as gene.
+
+    Parameters
+    ----------
+    pop_size: int
+        number of genes in one generation
+    elite_num: int
+        number of elite to keep
+    mutation_prob: float
+        probability of mutation of a knob in a gene
+    """
+    def __init__(self, task, pop_size, elite_num=3, mutation_prob=0.1):
+        super(GATuner, self).__init__(task)
+
+        # algorithm configurations
+        self.pop_size = pop_size
+        self.elite_num = elite_num
+        self.mutation_prob = mutation_prob
+
+        assert elite_num <= pop_size, "The number of elites must be less than population size"
+
+        # space info
+        self.space = task.config_space
+        self.dims = [len(x) for x in self.space.space_map.values()]
+
+        self.visited = set([])
+
+        # current generation
+        self.genes = []
+        self.scores = []
+        self.elites = []
+        self.elite_scores = []
+        self.trial_pt = 0
+
+        # random initialization
+        self.pop_size = min(self.pop_size, len(self.space))
+        for _ in range(self.pop_size):
+            tmp_gene = point2knob(np.random.randint(len(self.space)), self.dims)
+            while knob2point(tmp_gene, self.dims) in self.visited:
+                tmp_gene = point2knob(np.random.randint(len(self.space)), self.dims)
+
+            self.genes.append(tmp_gene)
+            self.visited.add(knob2point(tmp_gene, self.dims))
+
+    def next_batch(self, batch_size):
+        ret = []
+        for _ in range(batch_size):
+            gene = self.genes[self.trial_pt % self.pop_size]
+            self.trial_pt += 1
+            ret.append(self.space.get(knob2point(gene, self.dims)))
+
+        return ret
+
+    def update(self, inputs, results):
+        for inp, res in zip(inputs, results):
+            if res.error_no == 0:
+                y = inp.task.flop / np.mean(res.costs)
+                self.scores.append(y)
+            else:
+                self.scores.append(0)
+
+        if len(self.scores) >= len(self.genes):
+            genes = self.genes + self.elites
+            scores = np.array(self.scores[:len(self.genes)] + self.elite_scores)
+
+            # reserve elite
+            self.elites, self.elite_scores = [], []
+            elite_indexes = np.argpartition(scores, -self.elite_num)[-self.elite_num:]
+            for ind in elite_indexes:
+                self.elites.append(genes[ind])
+                self.elite_scores.append(scores[ind])
+
+            # cross over
+            indices = np.arange(len(genes))
+            scores /= np.max(scores)
+            probs = scores / np.sum(scores)
+            tmp_genes = []
+            for _ in range(self.pop_size):
+                p1, p2 = np.random.choice(indices, size=2, replace=False, p=probs)
+                p1, p2 = genes[p1], genes[p2]
+                point = np.random.randint(len(self.dims))
+                tmp_gene = p1[:point] + p2[point:]
+                tmp_genes.append(tmp_gene)
+
+            # mutation
+            next_genes = []
+            for tmp_gene in tmp_genes:
+                for j, dim in enumerate(self.dims):
+                    if np.random.random() < self.mutation_prob:
+                        tmp_gene[j] = np.random.randint(dim)
+
+                if len(self.visited) < len(self.space):
+                    while knob2point(tmp_gene, self.dims) in self.visited:
+                        j = np.random.randint(len(self.dims))
+                        tmp_gene[j] = np.random.randint(self.dims[j])
+                    next_genes.append(tmp_gene)
+                    self.visited.add(knob2point(tmp_gene, self.dims))
+                else:
+                    break
+
+            self.genes = next_genes
+            self.trial_pt = 0
+            self.scores = []
+
+    def has_next(self):
+        return len(self.visited) - (len(self.genes) - self.trial_pt) < len(self.space)

python/tvm/autotvm/tuner/gridsearch_tuner.py

+# pylint: disable=abstract-method
+"""Grid search tuner and random tuner"""
+
+import numpy as np
+
+from .tuner import Tuner
+
+
+class GridSearchTuner(Tuner):
+    """Enumerate the search space in a grid search order"""
+    def __init__(self, task):
+        super(GridSearchTuner, self).__init__(task)
+        self.counter = 0
+
+    def next_batch(self, batch_size):
+        ret = []
+        for _ in range(batch_size):
+            if self.counter >= len(self.task.config_space):
+                continue
+            index = self.counter
+            ret.append(self.task.config_space.get(index))
+            self.counter = self.counter + 1
+        return ret
+
+    def has_next(self):
+        return self.counter < len(self.task.config_space)
+
+    def __getstate__(self):
+        return {"counter": self.counter}
+
+    def __setstate__(self, state):
+        self.counter = state['counter']
+
+
+class RandomTuner(Tuner):
+    """Enumerate the search space in a random order"""
+    def __init__(self, task):
+        super(RandomTuner, self).__init__(task)
+        self.visited = set()
+
+    def next_batch(self, batch_size):
+        ret = []
+        counter = 0
+        while counter < batch_size:
+            if len(self.visited) >= len(self.task.config_space):
+                break
+            index = np.random.randint(len(self.task.config_space))
+            while index in self.visited:
+                index = np.random.randint(len(self.task.config_space))
+
+            ret.append(self.task.config_space.get(index))
+            self.visited.add(index)
+            counter += 1
+        return ret
+
+    def has_next(self):
+        return len(self.visited) < len(self.task.config_space)
+
+    def __getstate__(self):
+        return {"visited": self.counter}
+
+    def __setstate__(self, state):
+        self.counter = state['visited']

python/tvm/autotvm/tuner/metric.py

+# pylint: disable=invalid-name
+"""Metrics for evaluating tuning process"""
+
+import numpy as np
+
+from ..util import get_rank
+
+def max_curve(trial_scores):
+    """ f(n) = max([s[i] fo i < n])
+
+    Parameters
+    ----------
+    trial_scores: Array of float
+        the score of i th trial
+
+    Returns
+    -------
+    curve: Array of float
+        function values
+    """
+    ret = np.empty(len(trial_scores))
+    keep = -1e9
+    for i, score in enumerate(trial_scores):
+        keep = max(keep, score)
+        ret[i] = keep
+    return ret
+
+def mean_curve(trial_scores):
+    """ f(n) = mean([s[i] fo i < n])
+
+    Parameters
+    ----------
+    trial_scores: Array of float
+        the score of i th trial
+
+    Returns
+    -------
+    curve: Array of float
+        function values
+    """
+    ret = np.empty(len(trial_scores))
+    keep = 0
+    for i, score in enumerate(trial_scores):
+        keep += score
+        ret[i] = keep / (i+1)
+    return ret
+
+def recall_curve(trial_ranks, top=None):
+    """
+    if top is None, f(n) = sum([I(rank[i] < n) for i < n]) / n
+    if top is K,    f(n) = sum([I(rank[i] < K) for i < n]) / K
+
+    Parameters
+    ----------
+    trial_ranks: Array of int
+        the rank of i th trial in labels
+    top: int or None
+        top-n recall
+
+    Returns
+    -------
+    curve: Array of float
+        function values
+    """
+    if not isinstance(trial_ranks, np.ndarray):
+        trial_ranks = np.array(trial_ranks)
+
+    ret = np.zeros(len(trial_ranks))
+    if top is None:
+        for i in range(len(trial_ranks)):
+            ret[i] = np.sum(trial_ranks[:i] <= i) / (i+1)
+    else:
+        for i in range(len(trial_ranks)):
+            ret[i] = 1.0 * np.sum(trial_ranks[:i] < top) / top
+    return ret
+
+def cover_curve(trial_ranks):
+    """
+    f(n) = max k s.t. {1,2,...,k} is a subset of {ranks[i] for i < n}
+
+    Parameters
+    ----------
+    trial_ranks: Array of int
+        the rank of i th trial in labels
+
+    Returns
+    -------
+    curve: Array of float
+        function values
+    """
+    ret = np.empty(len(trial_ranks))
+    keep = -1
+    cover = set()
+    for i, rank in enumerate(trial_ranks):
+        cover.add(rank)
+        while keep+1 in cover:
+            keep += 1
+        ret[i] = keep + 1
+    return ret / len(trial_ranks)
+
+def average_recall(preds, labels, N):
+    """evaluate average recall-n for predictions and labels"""
+    trials = np.argsort(preds)[::-1]
+    ranks = get_rank(labels[trials])
+    curve = recall_curve(ranks)
+    return np.sum(curve[:N]) / N

python/tvm/autotvm/tuner/model_based_tuner.py

+# pylint: disable=no-else-return,invalid-name,consider-using-enumerate,abstract-method
+"""Base class for model-based tuner
+This type of tuner will fit a cost model and use some optimization methods to
+find optimums points of cost model in space.
+"""
+import gc
+
+import numpy as np
+
+from .tuner import Tuner
+
+
+class FeatureCache(object):
+    """Feature cache manager for cache sharing between different cost models"""
+    def __init__(self):
+        self.feature_cache = {}
+
+    def get(self, key):
+        """ Get feature cache dictionary for a key
+
+        Parameters
+        ----------
+        key: str
+            The key of a feature type
+
+        Returns
+        -------
+        fea_cache: dict
+            cache dictionary
+        """
+        if key not in self.feature_cache:
+            self.feature_cache[key] = {}
+
+        return self.feature_cache[key]
+
+    def size(self, key):
+        """" Get the size of a feature cache dictionary
+
+        Parameters
+        ----------
+        key: str
+            The key of a feature type
+
+        Returns
+        -------
+        n: int
+        """
+        return len(self.feature_cache.get(key, tuple()))
+
+    def clear(self, key):
+        """Clear feature cache for a key
+
+        Parameters
+        ----------
+        key: str
+            The key of a feature type
+        """
+        del self.feature_cache[key]
+        self.feature_cache[key] = {}
+        gc.collect()
+
+
+class CostModel(object):
+    """Cost model to predict the speed of a config"""
+    def __init__(self):
+        pass
+
+    def fit(self, xs, ys, plan_size):
+        """Fit to training data
+
+        Parameters
+        ----------
+        xs: Array of int
+            indexes of configs in the config space
+        ys: Array of float
+            The speed (flop, float number operations per second)
+        plan_size: int
+            The plan size of tuner
+        """
+        raise NotImplementedError()
+
+    def fit_log(self, records, plan_size):
+        """Fit training data from log.
+
+        Parameters
+        ----------
+        records: Array of Tuple(MeasureInput, MeasureResult)
+            The tuning records
+        plan_size: int
+            The plan size of tuner
+        """
+        raise NotImplementedError()
+
+    def predict(self, xs, output_margin=False):
+        """Predict the speed of configs
+
+        Parameters
+        ----------
+        xs: Array of int
+            The indexes of configs to predict
+        output_margin: bool, optional
+            Whether output the untransformed margin.
+            When a model is used as base model, it should output untransformed margin
+
+        Returns
+        -------
+        preds: Array of float
+            The prediction
+        """
+        raise NotImplementedError()
+
+    def load_basemodel(self, base_model):
+        """Load base model for transfer learning
+
+        Parameters
+        ----------
+        base_model: CostModel
+                base model
+        """
+        raise NotImplementedError()
+
+    def clone_new(self):
+        """Clone a new model with the same parameters.
+        This function will only copy hyperparameters of the tuner, not all the trained model
+
+        This is used for deriving a base model conveniently
+
+        Returns
+        -------
+        model: CostModel
+            A model with the same hyperparameter (argument)
+        """
+        raise NotImplementedError()
+
+
+class ModelOptimizer(object):
+    """Optimizer used to find optimal points of cost model"""
+    def __init__(self):
+        pass
+
+    def find_maximums(self, model, num, exclusive):
+        """Find maximum of a cost model
+
+        Note we use cost model to predict GFLOPS, so we should find the maximum
+
+        Parameters
+        ----------
+        model: CostModel
+            Cost model
+        num: int
+            The number of returned maximum points
+        exclusive: set, optional
+            The excluded set of this optimizer. Return results won't include any
+            elements in this set.
+        """
+        raise NotImplementedError()
+
+
+class ModelBasedTuner(Tuner):
+    """Base class for model based tuner
+    This type of tuner will fit a cost model and use an optimizer to
+    find the maximums of the cost model as next trials
+
+    Parameters
+    ----------
+    task: autotvm.task.Task
+        The tuning task
+    cost_model: CostModel
+        The cost model that predicts the speed of a config (IR)
+    model_optimizer:
+        The optimizer to find local optimum points of cost model in tuning search space
+    plan_size: int
+        Tuner will re-fit model per `plan_size` new measure samples
+    diversity_filter_ratio: int or float, optional
+        If is not None, the tuner will first select
+        top-(plan_size * diversity_filter_ratio) candidates according to the cost model
+        and then pick plan_size of them according to the diversity metric.
+    """
+
+    def __init__(self, task, cost_model, model_optimizer, plan_size, diversity_filter_ratio=None):
+        super(ModelBasedTuner, self).__init__(task)
+
+        # space
+        self.task = task
+        self.target = task.target
+        self.plan_size = plan_size
+        self.space = task.config_space
+        self.space_len = len(task.config_space)
+        self.dims = [len(x) for x in self.space.space_map.values()]
+
+        self.cost_model = cost_model
+        self.model_optimizer = model_optimizer
+        self.diversity_filter_ratio = diversity_filter_ratio
+
+        if self.diversity_filter_ratio:
+            assert self.diversity_filter_ratio >= 1, "Diversity filter ratio " \
+                                                     "must be larger than one"
+
+        # trial plan
+        self.trials = []
+        self.trial_pt = 0
+        self.visited = set()
+
+        # observed samples
+        self.xs = []
+        self.ys = []
+        self.flops_max = 0.0
+        self.train_ct = 0
+
+    def next_batch(self, batch_size):
+        ret = []
+
+        counter = 0
+        while counter < batch_size:
+            if len(self.visited) >= len(self.space):
+                break
+
+            while self.trial_pt < len(self.trials):
+                index = self.trials[self.trial_pt]
+                if index not in self.visited:
+                    break
+                self.trial_pt += 1
+
+            if self.trial_pt >= len(self.trials):  # trial list is empty, choose randomly
+                index = np.random.randint(len(self.space))
+                while index in self.visited:
+                    index = np.random.randint(len(self.space))
+
+            ret.append(self.space.get(index))
+            self.visited.add(index)
+
+            counter += 1
+        return ret
+
+    def update(self, inputs, results):
+        for inp, res in zip(inputs, results):
+            index = inp.config.index
+            if res.error_no == 0:
+                self.xs.append(index)
+                flops = inp.task.flop / np.mean(res.costs)
+                self.flops_max = max(self.flops_max, flops)
+                self.ys.append(flops)
+            else:
+                self.xs.append(index)
+                self.ys.append(0)
+
+        # if we have enough new training samples
+        if len(self.xs) >= self.plan_size * (self.train_ct + 1) \
+                and self.flops_max > 1e-6:
+            self.cost_model.fit(self.xs, self.ys, self.plan_size)
+            if self.diversity_filter_ratio:
+                candidate = self.model_optimizer.find_maximums(
+                    self.cost_model, self.plan_size * self.diversity_filter_ratio, self.visited)
+                scores = self.cost_model.predict(candidate)
+                knobs = [point2knob(x, self.dims) for x in candidate]
+                pick_index = submodular_pick(0 * scores, knobs, self.plan_size, knob_weight=1)
+                maximums = np.array(candidate)[pick_index]
+            else:
+                maximums = self.model_optimizer.find_maximums(
+                    self.cost_model, self.plan_size, self.visited)
+
+            self.trials = maximums
+            self.trial_pt = 0
+            self.train_ct += 1
+
+    def load_history(self, data_set):
+        base_model = self.cost_model.clone_new()
+        base_model.fit_log(data_set, self.plan_size)
+
+        if not self.trials:
+            # no plan yet, use base model to select initial trials
+            maximums = self.model_optimizer.find_maximums(base_model, self.visited)
+            self.trials = maximums
+            self.trial_pt = 0
+
+        self.cost_model.load_basemodel(base_model)
+
+    def has_next(self):
+        return len(self.visited) < len(self.space)
+
+
+def point2knob(p, dims):
+    """convert point form (single integer) to knob form (vector)"""
+    knob = []
+    for dim in dims:
+        knob.append(p % dim)
+        p //= dim
+    return knob
+
+
+def knob2point(knob, dims):
+    """convert knob form (vector) to point form (single integer)"""
+    p = 0
+    for j, k in enumerate(knob):
+        p += int(np.prod(dims[:j])) * k
+    return p
+
+
+def submodular_pick(scores, knobs, n_pick, knob_weight=1.0):
+    """Run greedy optimization to pick points with regard to both score and diversity.
+    DiversityScore = knob_weight * number of unique knobs in the selected set
+    Obj = sum(scores[i] for i in pick) + DiversityScore
+    Note that this objective function is a monotone submodular function.
+
+    Parameters
+    ----------
+    scores: Array of float
+        score of every points
+    knobs: Array of Array of int
+        feature vector (tunable knobs) of every points
+    n_pick: int
+        number of points to pick
+    knob_weight: float
+        weight of an unique knob feature
+    """
+    n = len(scores)
+    assert n == len(knobs)
+    n_knobs = len(knobs[0])
+
+    knobs_set = [set() for _ in range(n_knobs)]
+
+    ret = []
+    remain = list(range(len(scores)))
+
+    for _ in range(n_pick):
+        max_x = -1
+        max_delta = -1e9
+
+        for x in remain:
+            tmp_delta = scores[x]
+            for i in range(n_knobs):
+                if knobs[x][i] not in knobs_set[i]:
+                    tmp_delta += knob_weight
+
+            if tmp_delta > max_delta:
+                max_delta, max_x = tmp_delta, x
+
+        ret.append(max_x)
+        remain.remove(max_x)
+        for i in range(n_knobs):
+            knobs_set[i].add(knobs[max_x][i])
+
+    return ret

python/tvm/autotvm/tuner/sa_model_optimizer.py

+# pylint: disable=consider-using-enumerate
+"""
+Cost model optimizer based on simulated annealing
+"""
+
+import heapq
+import logging
+import time
+
+import numpy as np
+
+from ..util import sample_ints
+from .model_based_tuner import ModelOptimizer, knob2point, point2knob
+
+class SimulatedAnnealingOptimizer(ModelOptimizer):
+    """parallel simulated annealing optimization algorithm
+
+    Parameters
+    ----------
+    task: Task
+        The tuning task
+    n_iter: int
+        The number of iterations of simulated annealing
+    temp: float or Array of float
+        If is a single float, then use a constant temperature.
+        If is an Array, then perform linear cooling from temp[0] to temp[1]
+    early_stop: int, optional
+        Stop iteration if the optimal set do not change in `early_stop` rounds
+    verbose: int, optional
+        Print log every `verbose` iterations
+    """
+    def __init__(self, task, n_iter=500, temp=(1, 0), persistent=True, parallel_size=128,
+                 early_stop=30, verbose=50):
+        super(SimulatedAnnealingOptimizer, self).__init__()
+
+        self.task = task
+        self.dims = [len(x) for x in self.task.config_space.space_map.values()]
+
+        self.n_iter = n_iter
+        self.temp = temp
+        self.persistent = persistent
+        self.parallel_size = parallel_size
+        self.early_stop = early_stop
+        self.verbose = verbose
+        self.points = None
+
+    def find_maximums(self, model, num, exclusive):
+        tic = time.time()
+        temp, n_iter, early_stop, verbose = self.temp, self.n_iter, self.early_stop, self.verbose
+
+        if self.persistent and self.points is not None:
+            points = self.points
+        else:
+            points = np.array(sample_ints(0, len(self.task.config_space), self.parallel_size))
+
+        scores = model.predict(points)
+
+        # build heap and insert initial points
+        heap_items = [(float('-inf'), -i) for i in range(num)]
+        heapq.heapify(heap_items)
+        in_heap = set(exclusive)
+        in_heap.update([-i for i in range(num)])
+
+        for s, p in zip(scores, points):
+            if s > heap_items[0][0] and p not in in_heap:
+                pop = heapq.heapreplace(heap_items, (s, p))
+                in_heap.remove(pop[1])
+                in_heap.add(p)
+
+        k = 0
+        k_last_modify = 0
+
+        if isinstance(temp, (tuple, list, np.ndarray)):
+            t = temp[0]
+            cool = 1.0 * (temp[0] - temp[1]) / (n_iter + 1)
+        else:
+            t = temp
+            cool = 0
+
+        while k < n_iter and k < k_last_modify + early_stop:
+            new_points = np.empty_like(points)
+            for i, p in enumerate(points):
+                new_points[i] = random_walk(p, self.dims)
+
+            new_scores = model.predict(new_points)
+
+            ac_prob = np.exp((new_scores - scores) / t)
+            ac_index = np.random.random(len(ac_prob)) < ac_prob
+
+            points[ac_index] = new_points[ac_index]
+            scores[ac_index] = new_scores[ac_index]
+
+            for s, p in zip(new_scores, new_points):
+                if s > heap_items[0][0] and p not in in_heap:
+                    pop = heapq.heapreplace(heap_items, (s, p))
+                    in_heap.remove(pop[1])
+                    in_heap.add(p)
+                    k_last_modify = k
+
+            k += 1
+            t -= cool
+
+            if verbose >= 1 and k % verbose == 0:
+                t_str = "%.2f" % t
+                logging.info("SA iter: %d\tlast_update: %d\tmax-0: %.2f\tmax-1: %.2f\ttemp: %s\t"
+                             "elapsed: %.2f",
+                             k, k_last_modify, heap_items[0][0],
+                             np.max([v for v, _ in heap_items]), t_str,
+                             time.time() - tic)
+
+        heap_items.sort(key=lambda item: -item[0])
+        if verbose:
+            logging.info("SA iter: %d\tlast_update: %d\tmax-0: %.2f\tmax-1: %.2f\telapsed: %.2f",
+                         k, k_last_modify, heap_items[-1][0], heap_items[0][0], time.time() - tic)
+            logging.info("SA Maximums: %s", heap_items)
+
+        if self.persistent:
+            self.points = points
+
+        return [x[1] for x in heap_items]
+
+def random_walk(p, dims):
+    """random walk as local transition
+
+    Parameters
+    ----------
+    p: int
+        index of the ConfigEntity
+    dims: Array of int
+        sizes of each dimension
+
+    Returns
+    -------
+    new_p: int
+        new neighborhood index
+    """
+    # transform to knob form
+    old = point2knob(p, dims)
+    new = list(old)
+
+    # mutate
+    while new == old:
+        from_i = np.random.randint(len(old))
+        to_v = np.random.randint(dims[from_i])
+        new[from_i] = to_v
+
+    # transform to index form
+    return knob2point(new, dims)

python/tvm/autotvm/tuner/tuner.py

+# pylint: disable=unused-argument, no-self-use, invalid-name
+"""Base class of tuner"""
+import logging
+
+import numpy as np
+
+from ..measure import MeasureInput
+from ..measure import create_measure_batch
+
+
+class Tuner(object):
+    """Base class for tuners
+
+    Parameters
+    ----------
+    task: autotvm.task.Task
+        Tuning Task
+    """
+
+    def __init__(self, task, **kwargs):
+        self.param = kwargs
+        self.recorder = None
+
+        self.task = task
+
+        # keep the current best
+        self.best_config = None
+        self.best_flops = 0
+        self.best_measure_pair = None
+
+    def has_next(self):
+        """Whether has next untried config in the space
+
+        Returns
+        -------
+        has_next: bool
+        """
+        raise NotImplementedError()
+
+    def next_batch(self, batch_size):
+        """get the next batch of configs to be measure on real hardware
+
+        Parameters
+        ----------
+        batch_size: int
+            The size of the batch
+
+        Returns
+        -------
+        a batch of configs
+        """
+        raise NotImplementedError()
+
+    def update(self, inputs, results):
+        """Update parameters of the tuner according to measurement results
+
+        Parameters
+        ----------
+        inputs: Array of autotvm.measure.MeasureInput
+            The input for measurement
+        results: Array of autotvm.measure.MeasureResult
+            result for measurement
+        """
+        pass
+
+    def tune(self, n_trial, measure_option, verbose=1, callbacks=()):
+        """Begin tuning
+
+        Parameters
+        ----------
+        n_trial: int
+            Maximum number of configs to try (measure on real hardware)
+        measure_option: dict
+            The options for how to measure generated code.
+            You should use the return value ot autotvm.measure_option for this argument.
+        verbose: int
+            0: silent mode, no output
+            1: print every measurement result
+        callbacks: List of callable
+            A list of callback functions. The signature of callback function is
+            (Tuner, List of MeasureInput, List of MeasureResult)
+            with no return value. These callback functions will be called on
+            every measurement pair. See autotvm/tuner/callback.py for some examples.
+        """
+        measure_batch = create_measure_batch(self.task, measure_option)
+        parallel_num = getattr(measure_batch, 'parallel_num', 1)
+
+        i = 0
+        while i < n_trial:
+            if not self.has_next():
+                break
+
+            configs = self.next_batch(min(parallel_num, n_trial - i))
+
+            inputs = [MeasureInput(self.task.target, self.task, config) for config in configs]
+            results = measure_batch(inputs)
+
+            # print info
+            if verbose >= 1:
+                for k, (inp, res) in enumerate(zip(inputs, results)):
+                    config = inp.config
+                    if res.error_no == 0:
+                        flops = inp.task.flop / np.mean(res.costs)
+                    else:
+                        flops = 0
+                    if flops > self.best_flops:
+                        self.best_flops = flops
+                        self.best_config = config
+                        self.best_measure_pair = (inp, res)
+
+                    logging.info("No: %d\tGFLOPS: %.2f/%.2f\tresult: %s\t%s",
+                                 i + k + 1, flops / 1e9, self.best_flops / 1e9,
+                                 res, config)
+
+            i += len(results)
+
+            self.update(inputs, results)
+
+            for callback in callbacks:
+                callback(self, inputs, results)
+
+        del measure_batch
+
+    def reset(self):
+        """reset the status of tuner"""
+        self.best_config = None
+        self.best_flops = 0
+        self.best_measure_pair = None
+
+    def load_history(self, data_set):
+        """load history data for transfer learning
+
+        Parameters
+        ----------
+        data_set: Array of (MeasureInput, MeasureResult) pair
+            Previous tuning records
+        """
+        raise NotImplementedError()

python/tvm/autotvm/tuner/xgboost_cost_model.py

+# pylint: disable=invalid-name
+"""XGBoost as cost model"""
+
+import multiprocessing
+import logging
+import time
+
+import numpy as np
+try:
+    import xgboost as xgb
+except ImportError:
+    xgb = None
+
+from .. import feature
+from ..util import get_rank
+from .metric import max_curve, recall_curve, cover_curve
+from .model_based_tuner import CostModel, FeatureCache
+
+class XGBoostCostModel(CostModel):
+    """XGBoost as cost model
+
+    Parameters
+    ----------
+    task: Task
+        The tuning task
+    feature_type: str, optional
+        If is 'itervar', use features extracted from IterVar (loop variable).
+        If is 'knob', use flatten ConfigEntity directly.
+        If is 'curve', use sampled curve feature (relation feature).
+
+        Note on choosing feature type:
+        For single task tuning, 'itervar' and 'knob' is good.
+                                'itervar' is more accurate but 'knob' is much faster.
+        For cross-shape tuning (e.g. many convolutions with different shapes),
+                               'itervar' and 'curve' has better transferability,
+                               'knob' is faster.
+        For cross-device or cross-operator tuning, you can use 'curve' only.
+    loss_type: str
+        If is 'reg', use regression loss to train cost model.
+                     The cost model predicts the normalized flops.
+        If is 'rank', use pairwise rank loss to train cost model.
+                     The cost model predicts relative rank score.
+    num_threads: int, optional
+        The number of threads.
+    verbose: int, optional
+        If is not none, the cost model will print training log every `verbose` iterations.
+    """
+    def __init__(self, task, feature_type, loss_type, num_threads=None, verbose=20):
+        super(XGBoostCostModel, self).__init__()
+
+        if xgb is None:
+            raise RuntimeError("XGBoost is required for XGBoostCostModel. "
+                               "Please install its python package first. "
+                               "Help: (https://xgboost.readthedocs.io/en/latest/) ")
+
+        self.task = task
+        self.target = task.target
+        self.space = task.config_space
+
+        self.fea_type = feature_type
+        self.loss_type = loss_type
+        self.num_threads = num_threads
+        self.verbose = verbose
+
+        if loss_type == 'reg':
+            self.xgb_params = {
+                'max_depth': 3,
+                'gamma': 0.0001,
+                'min_child_weight': 1,
+
+                'subsample': 1.0,
+
+                'eta': 0.3,
+                'lambda': 1.00,
+                'alpha': 0,
+
+                'objective': 'reg:linear',
+            }
+        elif loss_type == 'rank':
+            self.xgb_params = {
+                'max_depth': 3,
+                'gamma': 0.0001,
+                'min_child_weight': 1,
+
+                'subsample': 1.0,
+
+                'eta': 0.3,
+                'lambda': 1.00,
+                'alpha': 0,
+
+                'objective': 'rank:pairwise',
+            }
+        else:
+            raise RuntimeError("Invalid loss type: " + loss_type)
+
+        self.xgb_params['silent'] = 1
+        if num_threads:
+            self.xgb_params['nthread'] = num_threads
+        self.bst = None
+
+        if feature_type == 'itervar':
+            self.feature_extract_func = _extract_itervar_feature_index
+        elif feature_type == 'knob':
+            self.feature_extract_func = _extract_knob_feature_index
+        elif feature_type == 'curve':
+            self.feature_extract_func = _extract_curve_feature_index
+        else:
+            raise RuntimeError("Invalid feature type " + feature_type)
+
+        self.feature_cache = FeatureCache()
+        self.feature_extra_ct = 0
+        self.pool = None
+        self.base_model = None
+
+        self._reset_pool()
+
+    def _reset_pool(self):
+        # reset processing pool for feature extraction
+        if self.pool:
+            self.pool.terminate()
+            self.pool.join()
+            del self.pool
+        # use global variable to pass common arguments
+        global _extract_space, _extract_target, _extract_task
+        _extract_space = self.space
+        _extract_target = self.target
+        _extract_task = self.task
+        self.pool = multiprocessing.Pool(self.num_threads)
+
+    def fit(self, xs, ys, plan_size):
+        tic = time.time()
+        self._reset_pool()
+
+        x_train = self._get_feature(xs)
+        y_train = np.array(ys)
+        y_train /= np.max(y_train)
+
+        valid_index = y_train > 1e-6
+        index = np.random.permutation(len(x_train))
+        dtrain = xgb.DMatrix(x_train[index], y_train[index])
+
+        if self.base_model:
+            dtrain.set_base_margin(self.base_model.predict(xs, output_margin=True))
+
+        self.bst = xgb.train(self.xgb_params, dtrain,
+                             num_boost_round=8000,
+                             callbacks=[custom_callback(
+                                 stopping_rounds=20,
+                                 metric='tr-a-recall@%d' % plan_size,
+                                 evals=[(dtrain, 'tr')],
+                                 maximize=True,
+                                 fevals=[
+                                     xgb_average_recalln_curve_score(plan_size),
+                                 ],
+                                 verbose_eval=self.verbose)])
+
+        logging.info("train: %.2f\tobs: %d\terror: %d\tn_cache: %d",
+                     time.time() - tic, len(xs),
+                     len(xs) - np.sum(valid_index),
+                     self.feature_cache.size(self.fea_type))
+
+    def fit_log(self, records, plan_size):
+        tic = time.time()
+        self._reset_pool()
+
+        args = list(records)
+        if self.fea_type == 'itervar':
+            feature_extract_func = _extract_itervar_feature_log
+        elif self.fea_type == 'knob':
+            feature_extract_func = _extract_knob_feature_log
+        elif self.fea_type == 'curve':
+            feature_extract_func = _extract_curve_feature_log
+        else:
+            raise RuntimeError("Invalid feature type: " + self.fea_type)
+        res = self.pool.map(feature_extract_func, args)
+        xs, ys = zip(*res)
+        xs, ys = np.array(xs), np.array(ys)
+
+        x_train = xs
+        y_train = ys
+        y_train /= np.max(y_train)
+
+        index = np.random.permutation(len(x_train))
+        dtrain = xgb.DMatrix(x_train[index], y_train[index])
+
+        plan_size *= 2
+        self.bst = xgb.train(self.xgb_params, dtrain,
+                             num_boost_round=200,
+                             callbacks=[custom_callback(
+                                 stopping_rounds=100,
+                                 metric='tr-a-recall@%d' % plan_size,
+                                 evals=[(dtrain, 'tr')],
+                                 maximize=True,
+                                 fevals=[
+                                     xgb_average_recalln_curve_score(plan_size),
+                                 ],
+                                 verbose_eval=self.verbose)])
+
+        logging.info("train: %.2f\tobs: %d", time.time() - tic, len(xs))
+
+    def predict(self, xs, output_margin=False):
+        feas = self._get_feature(xs)
+        dtest = xgb.DMatrix(feas)
+
+        if self.base_model:
+            dtest.set_base_margin(self.base_model.predict(xs, output_margin=True))
+
+        return self.bst.predict(dtest, output_margin=output_margin)
+
+    def load_basemodel(self, base_model):
+        self.base_model = base_model
+
+    def clone_new(self):
+        return XGBoostCostModel(self.task, self.fea_type, self.loss_type,
+                                self.num_threads, self.verbose)
+
+    def _get_feature(self, indexes):
+        """get features for indexes, run extraction if we do not have cache for them"""
+        # free feature cache
+        if self.feature_cache.size(self.fea_type) >= 100000:
+            self.feature_cache.clear(self.fea_type)
+
+        fea_cache = self.feature_cache.get(self.fea_type)
+
+        indexes = np.array(indexes)
+        need_extract = [x for x in indexes if x not in fea_cache]
+
+        if need_extract:
+            feas = self.pool.map(self.feature_extract_func, need_extract)
+            for i, fea in zip(need_extract, feas):
+                fea_cache[i] = fea
+
+        ret = np.empty((len(indexes), fea_cache[indexes[0]].shape[-1]), dtype=np.float32)
+        for i, ii in enumerate(indexes):
+            ret[i, :] = fea_cache[ii]
+        return ret
+
+
+_extract_space = None
+_extract_target = None
+_extract_task = None
+
+def _extract_itervar_feature_index(index):
+    """extract iteration var feature for an index in extract_space"""
+    config = _extract_space.get(index)
+    with _extract_target:
+        sch, args = _extract_task.instantiate(config)
+    fea = feature.get_itervar_feature_flatten(sch, args, take_log=True)
+    fea = np.concatenate((fea, list(config.get_other_option().values())))
+    return fea
+
+def _extract_itervar_feature_log(arg):
+    """extract iteration var feature for log items"""
+    inp, res = arg
+    config = inp.config
+    with inp.target:
+        sch, args = inp.task.instantiate(config)
+    fea = feature.get_itervar_feature_flatten(sch, args, take_log=True)
+    x = np.concatenate((fea, list(config.get_other_option().values())))
+
+    if res.error_no == 0:
+        y = inp.task.flop / np.mean(res.costs)
+    else:
+        y = 0
+    return x, y
+
+def _extract_knob_feature_index(index):
+    """extract knob feature for an index in extract_space"""
+    config = _extract_space.get(index)
+    return config.get_flatten_feature()
+
+def _extract_knob_feature_log(arg):
+    """extract knob feature for log items"""
+    inp, res = arg
+    config = inp.config
+    x = config.get_flatten_feature()
+
+    if res.error_no == 0:
+        with inp.target:  # necessary, for calculating flops of this task
+            inp.task.instantiate(config)
+        y = inp.task.flop / np.mean(res.costs)
+    else:
+        y = 0
+    return x, y
+
+def _extract_curve_feature_index(index):
+    """extract sampled curve feature for an index in extract_space"""
+    config = _extract_space.get(index)
+    with _extract_target:
+        sch, args = _extract_task.instantiate(config)
+    fea = feature.get_buffer_curve_sample_flatten(sch, args, sample_n=20)
+    fea = np.concatenate((fea, list(config.get_other_option().values())))
+    return np.array(fea)
+
+def _extract_curve_feature_log(arg):
+    """extract sampled curve feature for log items"""
+    inp, res = arg
+    config = inp.config
+    with inp.target:
+        sch, args = inp.task.instantiate(config)
+    fea = feature.get_buffer_curve_sample_flatten(sch, args, sample_n=20)
+    x = np.concatenate((fea, list(config.get_other_option().values())))
+
+    if res.error_no == 0:
+        y = inp.task.flop / np.mean(res.costs)
+    else:
+        y = 0
+    return x, y
+
+
+def custom_callback(stopping_rounds, metric, fevals, evals=(), log_file=None,
+                    save_file="xgb_checkpoint", save_every=None,
+                    maximize=False, verbose_eval=True):
+    """callback function for xgboost to support multiple custom evaluation functions"""
+    from xgboost.core import EarlyStopException
+    from xgboost.callback import _fmt_metric
+    from xgboost.training import aggcv
+
+    state = {}
+    metric_shortname = metric.split("-")[1]
+
+    def init(env):
+        """internal function"""
+        bst = env.model
+
+        state['maximize_score'] = maximize
+        state['best_iteration'] = 0
+        if maximize:
+            state['best_score'] = float('-inf')
+        else:
+            state['best_score'] = float('inf')
+
+        if bst is not None:
+            if bst.attr('best_score') is not None:
+                state['best_score'] = float(bst.attr('best_score'))
+                state['best_iteration'] = int(bst.attr('best_iteration'))
+                state['best_msg'] = bst.attr('best_msg')
+            else:
+                bst.set_attr(best_iteration=str(state['best_iteration']))
+                bst.set_attr(best_score=str(state['best_score']))
+        else:
+            assert env.cvfolds is not None
+
+    def callback(env):
+        """internal function"""
+        if not state:
+            init(env)
+
+        bst = env.model
+        i = env.iteration
+        cvfolds = env.cvfolds
+
+        res_dict = {}
+
+        ##### evaluation #####
+        if cvfolds is not None:
+            for feval in fevals:
+                tmp = aggcv([f.eval(i, feval) for f in cvfolds])
+                for k, mean, std in tmp:
+                    res_dict[k] = [mean, std]
+        else:
+            for feval in fevals:
+                bst_eval = bst.eval_set(evals, i, feval)
+                res = [x.split(':') for x in bst_eval.split()]
+                for kv in res[1:]:
+                    res_dict[kv[0]] = [float(kv[1])]
+
+        eval_res = []
+        keys = list(res_dict.keys())
+        keys.sort(key=lambda x: x if metric_shortname not in x else "a" + x)
+        for key in keys:
+            v = res_dict[key]
+            eval_res.append([key] + v)
+
+        ##### print eval result #####
+        infos = ["XGB iter: %3d" % i]
+        for item in eval_res:
+            if 'null' in item[0]:
+                continue
+            infos.append("%s: %.6f" % (item[0], item[1]))
+
+        if not isinstance(verbose_eval, bool) and i % verbose_eval == 0:
+            logging.info("\t".join(infos))
+        if log_file:
+            with open(log_file, "a") as fout:
+                fout.write("\t".join(infos) + '\n')
+
+        ##### save model #####
+        if save_every and i % save_every == 0:
+            filename = save_file + ".%05d.bst" % i
+            logging.info("save model to %s ...", filename)
+            bst.save_model(filename)
+
+        ##### choose score and do early stopping #####
+        score = None
+        for item in eval_res:
+            if item[0] == metric:
+                score = item[1]
+                break
+        assert score is not None
+
+        best_score = state['best_score']
+        best_iteration = state['best_iteration']
+        maximize_score = state['maximize_score']
+        if (maximize_score and score > best_score) or \
+                (not maximize_score and score < best_score):
+            msg = '[%d] %s' % (
+                env.iteration,
+                '\t'.join([_fmt_metric(x) for x in eval_res]))
+            state['best_msg'] = msg
+            state['best_score'] = score
+            state['best_iteration'] = env.iteration
+            # save the property to attributes, so they will occur in checkpoint.
+            if env.model is not None:
+                env.model.set_attr(best_score=str(state['best_score']),
+                                   best_iteration=str(state['best_iteration']),
+                                   best_msg=state['best_msg'])
+        elif env.iteration - best_iteration >= stopping_rounds:
+            best_msg = state['best_msg']
+            if verbose_eval and env.rank == 0:
+                logging.info("Stopping. Best iteration: %s ", best_msg)
+            raise EarlyStopException(best_iteration)
+
+    return callback
+
+
+# feval wrapper for xgboost
+def xgb_max_curve_score(N):
+    """evaluate max curve score for xgb"""
+    def feval(preds, labels):
+        labels = labels.get_label()
+        trials = np.argsort(preds)[::-1]
+        scores = labels[trials]
+        curve = max_curve(scores)
+        return "Smax@%d" % N, curve[N] / np.max(labels)
+    return feval
+
+def xgb_recalln_curve_score(N):
+    """evaluate recall-n curve score for xgb"""
+    def feval(preds, labels):
+        labels = labels.get_label()
+        trials = np.argsort(preds)[::-1]
+        ranks = get_rank(labels[trials])
+        curve = recall_curve(ranks)
+        return "recall@%d" % N, curve[N]
+    return feval
+
+def xgb_average_recalln_curve_score(N):
+    """evaluate average recall-n curve score for xgb"""
+    def feval(preds, labels):
+        labels = labels.get_label()
+        trials = np.argsort(preds)[::-1]
+        ranks = get_rank(labels[trials])
+        curve = recall_curve(ranks)
+        return "a-recall@%d" % N, np.sum(curve[:N]) / N
+    return feval
+
+def xgb_recallk_curve_score(N, topk):
+    """evaluate recall-k curve score for xgb"""
+    def feval(preds, labels):
+        labels = labels.get_label()
+        trials = np.argsort(preds)[::-1]
+        ranks = get_rank(labels[trials])
+        curve = recall_curve(ranks, topk)
+        return "recall@%d" % topk, curve[N]
+    return feval
+
+def xgb_cover_curve_score(N):
+    """evaluate cover curve score for xgb"""
+    def feval(preds, labels):
+        labels = labels.get_label()
+        trials = np.argsort(preds)[::-1]
+        ranks = get_rank(labels[trials])
+        curve = cover_curve(ranks)
+        return "cover@%d" % N, curve[N]
+    return feval
+
+def xgb_null_score(_):
+    """empty score function for xgb"""
+    def feval(__, ___):
+        return "null", 0
+    return feval

python/tvm/autotvm/tuner/xgboost_tuner.py

+"""Tuner that uses xgboost as cost model"""
+
+from .model_based_tuner import ModelBasedTuner, ModelOptimizer
+from .xgboost_cost_model import XGBoostCostModel
+from .sa_model_optimizer import SimulatedAnnealingOptimizer
+
+class XGBTuner(ModelBasedTuner):
+    """Tuner that uses xgboost as cost model
+
+    Parameters
+    ----------
+    task: Task
+        The tuning task
+    plan_size: int
+        The size of a plan. After `plan_size` trials, the tuner will refit a new cost model
+        and do planing for the next `plan_size` trials.
+    feature_type: str, optional
+        If is 'itervar', use features extracted from IterVar (loop variable).
+        If is 'knob', use flatten ConfigEntity directly.
+        If is 'curve', use sampled curve feature (relation feature).
+
+        Note on choosing feature type:
+        For single task tuning, 'itervar' and 'knob' is good.
+                                'itervar' is more accurate but 'knob' is much faster.
+        For cross-shape tuning (e.g. many convolutions with different shapes),
+                               'itervar' and 'curve' has better transferability,
+                               'knob' is faster.
+        For cross-device or cross-operator tuning, you can use 'curve' only.
+    loss_type: str
+        If is 'reg', use regression loss to train cost model.
+                     The cost model predicts the normalized flops.
+        If is 'rank', use pairwise rank loss to train cost model.
+                     The cost model predicts relative rank score.
+    num_threads: int, optional
+        The number of threads.
+    optimizer: str or ModelOptimizer, optional
+        If is 'sa', use a default simulated annealing optimizer.
+        Otherwise it should be a ModelOptimizer object.
+    diversity_filter_ratio: int or float, optional
+        If is not None, the tuner will first select
+        top-(plan_size * diversity_filter_ratio) candidates according to the cost model
+        and then pick batch_size of them according to the diversity metric.
+    """
+    def __init__(self, task, plan_size=32,
+                 feature_type='itervar', loss_type='rank', num_threads=None,
+                 optimizer='sa', diversity_filter_ratio=None):
+        cost_model = XGBoostCostModel(task,
+                                      feature_type=feature_type,
+                                      loss_type=loss_type,
+                                      num_threads=num_threads)
+        if optimizer == 'sa':
+            optimizer = SimulatedAnnealingOptimizer(task)
+        else:
+            assert isinstance(optimizer, ModelOptimizer), "Optimizer must be " \
+                                                          "a supported name string" \
+                                                          "or a ModelOptimizer object."
+
+        super(XGBTuner, self).__init__(task, cost_model, optimizer,
+                                       plan_size, diversity_filter_ratio)

python/tvm/autotvm/util.py

+# pylint: disable=invalid-name
+"""Utilities"""
+import logging
+import multiprocessing
+import time
+
+import numpy as np
+
+from .. import expr, ir_pass
+
+def get_rank(values):
+    """get rank of items
+
+    Parameters
+    ----------
+    values: Array
+
+    Returns
+    -------
+    ranks: Array of int
+        the rank of this item in the input (the largest value ranks first)
+    """
+    tmp = np.argsort(-values)
+    ranks = np.empty_like(tmp)
+    ranks[tmp] = np.arange(len(tmp))
+    return ranks
+
+
+def sample_ints(low, high, m):
+    """
+    Sample m different integer numbers from [low, high) without replacement
+    This function is an alternative of `np.random.choice` when (high - low) > 2 ^ 32, in
+    which case numpy does not work.
+
+    Parameters
+    ----------
+    low: int
+        low point of sample range
+    high: int
+        high point of sample range
+    m: int
+        The number of sampled int
+
+    Returns
+    -------
+    ints: an array of size m
+    """
+    vis = set()
+    assert m <= high - low
+    while len(vis) < m:
+        new = np.random.randint(low, high)
+        while new in vis:
+            new = np.random.randint(low, high)
+        vis.add(new)
+
+    return list(vis)
+
+
+def pool_map(func, args, batch_size, verbose=False, pool=None):
+    """A wrapper of multiprocessing.pool.Pool.map to support small-batch mapping
+    for large argument list. This can reduce memory usage
+
+    Parameters
+    ----------
+    func: Func(arg) -> np.ndarray
+        mapping function
+    args: List
+        list of arguments
+    batch_size: int
+        batch size in mapping
+    verbose: bool, optional
+        whether print progress
+    pool: multiprocessing.Pool, optional
+        pool objection
+
+    Returns
+    -------
+    converted numpy array
+    """
+
+    ret = None
+    tic = time.time()
+    local_pool = pool or multiprocessing.Pool()
+    if verbose:
+        logging.info("mapping begin")
+    for i in range(0, len(args), batch_size):
+        if verbose:
+            logging.info("mapping %d/%d elapsed %.2f", i, len(args),
+                         time.time() - tic)
+        tmp = np.array(local_pool.map(func, args[i:i+batch_size]))
+        ret = tmp if ret is None else np.concatenate((ret, tmp))
+    if verbose:
+        logging.info("mapping done")
+    if not pool:
+        local_pool.close()
+    return ret
+
+def get_func_name(func):
+    """Get name of a function
+
+    Parameters
+    ----------
+    func: Function
+        The function
+    Returns
+    -------
+    name: str
+        The name
+    """
+
+    return func.func_name if hasattr(func, 'func_name') else func.__name__
+
+
+def get_const_int(exp):
+    """Verifies expr is integer and get the constant value.
+
+    Parameters
+    ----------
+    exp : tvm.Expr or int
+        The input expression.
+
+    Returns
+    -------
+    out_value : int
+        The output.
+    """
+    if isinstance(exp, int):
+        return exp
+    if not isinstance(exp, (expr.IntImm, expr.UIntImm)):
+        exp = ir_pass.Simplify(expr)
+    if not isinstance(exp, (expr.IntImm, expr.UIntImm)):
+        raise ValueError("Expect value to be constant int")
+    return exp.value
+
+
+def get_const_tuple(in_tuple):
+    """Verifies input tuple is IntImm, returns tuple of int.
+
+    Parameters
+    ----------
+    in_tuple : tuple of Expr
+        The input.
+
+    Returns
+    -------
+    out_tuple : tuple of int
+        The output.
+    """
+    return tuple(get_const_int(x) for x in in_tuple)

python/tvm/rpc/client.py

@@ -229,8 +229,14 @@ class TrackerSession(object):
         res += "----------------------------\n"
         res += "key\tfree\tpending\n"
         res += "----------------------------\n"
-        for k, v in data["queue_info"].items():
-            res += "%s\t%d\t%g\n" % (k, v["free"], v["pending"])
+        queue_info = data['queue_info']
+        keys = list(queue_info.keys())
+        if keys:
+            keys.sort()
+            max_key_len = max([len(k) for k in keys])
+            for k in keys:
+                res += ("%%-%d" % max_key_len + "s\t%d\t%g\n") % \
+                       (k, queue_info[k]["free"], queue_info[k]["pending"])
         res += "----------------------------\n"
         return res
 

src/autotvm/feature_visitor.cc

+/*!
+ *  Copyright (c) 2018 by Contributors
+ * \file feature_visitor.cc
+ * \brief Base class for feature extractor.
+ *        These features are used for machine learning cost model
+ */
+
+#include "feature_visitor.h"
+
+namespace tvm {
+namespace autotvm {
+
+// for loop
+void FeatureVisitor::Visit_(const For *op) {
+  const auto *extent = op->extent.as<IntImm>();
+  int64_t loop_extent = -1;
+  if (extent != nullptr)
+    loop_extent = extent->value;
+  AnnotationType ann = kSerial;
+  switch (op->for_type) {
+    case ForType ::Parallel:
+      ann = kParallel;
+      break;
+    case ForType::Unrolled:
+      ann = kUnrolled;
+      break;
+    case ForType::Vectorized:
+      ann = kVectorized;
+      break;
+    case ForType::Serial:
+      ann = kSerial;
+      break;
+  }
+
+  if (EnterItervar_(op->loop_var, loop_extent, ann)) {
+    IRVisitor::Visit_(op);
+    ExitItervar_();
+  }
+}
+
+// parallel axis, virtual thread
+void FeatureVisitor::Visit_(const AttrStmt *op) {
+  if (op->attr_key == attr::thread_extent ||
+      op->attr_key == attr::virtual_thread) {
+    VarExpr var = op->node.as<tvm::IterVarNode>()->var;
+    const auto *extent = op->value.as<IntImm>();
+    CHECK(extent);
+
+    std::string name = var.get()->name_hint;
+    AnnotationType ann = kParallel;
+    if (op->attr_key == attr::thread_extent) {
+      if (name == "blockIdx.x")
+        ann = kBlockX;
+      else if (name == "blockIdx.y")
+        ann = kBlockY;
+      else if (name == "blockIdx.z")
+        ann = kBlockZ;
+      else if (name == "threadIdx.x")
+        ann = kThreadX;
+      else if (name == "threadIdx.y")
+        ann = kThreadY;
+      else if (name == "threadIdx.z")
+        ann = kThreadZ;
+      else
+        LOG(FATAL) << "invalid thread itervar " + name;
+    } else {
+      ann = kVirtualThread;
+    }
+
+    if (EnterItervar_(var, extent->value, ann)) {
+      IRVisitor::Visit_(op);
+      ExitItervar_();
+    }
+  } else {
+    IRVisitor::Visit_(op);
+  }
+}
+
+// memory access
+void FeatureVisitor::Visit_(const Load *op) {
+  EnterMem_(op->buffer_var, op->index);
+  IRVisitor::Visit_(op);
+  ExitMem_();
+}
+
+void FeatureVisitor::Visit_(const Store *op) {
+  EnterMem_(op->buffer_var, op->index);
+  IRVisitor::Visit_(op);
+  ExitMem_();
+}
+
+}  // namespace autotvm
+}  // namespace tvm

src/autotvm/feature_visitor.h

+/*!
+ *  Copyright (c) 2018 by Contributors
+ * \file feature_visitor.h
+ * \brief Base class for feature extractor.
+ *        These features are used for machine learning cost model
+ */
+
+#ifndef TVM_AUTOTVM_FEATURE_VISITOR_H_
+#define TVM_AUTOTVM_FEATURE_VISITOR_H_
+
+#include <tvm/ir.h>
+#include <tvm/ir_visitor.h>
+#include <string>
+
+namespace tvm {
+namespace autotvm {
+
+using namespace tvm::ir;
+
+/*!
+ * \brief Type of for loop, used as one-hot encoding in features
+ */
+enum AnnotationType {
+  kBlockX, kBlockY, kBlockZ, kThreadX, kThreadY, kThreadZ,
+  kUnrolled, kVectorized, kParallel, kSerial, kVirtualThread,
+  kNum,
+};
+
+/*!
+ * \brief A base class for feature extractor, used for processing
+ * for loop and memory access in the IR
+ */
+class FeatureVisitor : public IRVisitor {
+ public:
+  // for loop
+  void Visit_(const For *op);
+  void Visit_(const AttrStmt *op);
+
+  // memory access
+  void Visit_(const Load *op);
+  void Visit_(const Store *op);
+
+ protected:
+  /*!
+ * \brief Enter a for loop node
+ * \param var The expression to be printed.
+ * \param length The output stream
+ * \param ann_type The type for the for loop
+ * \return skip Whether skip this node
+ */
+  virtual bool EnterItervar_(tvm::VarExpr var, int64_t length, AnnotationType ann_type) = 0;
+  /*! \brief Exit a for loop subtree */
+  virtual void ExitItervar_() = 0;
+  /*!
+   * \brief Enter a memory access node
+   * \param buffer_var The buffer to access.
+   * \param index Index expression
+   */
+  virtual void EnterMem_(tvm::VarExpr buffer_var, tvm::Expr index) = 0;
+  /*! \brief Exit a memory access node */
+  virtual void ExitMem_() = 0;
+};
+
+}  // namespace autotvm
+}  // namespace tvm
+
+#endif  // TVM_AUTOTVM_FEATURE_VISITOR_H_

src/autotvm/touch_extractor.cc

+/*!
+ *  Copyright (c) 2018 by Contributors
+ * \file touch_extractor.cc
+ * \brief Extract feature of touch pattern of axes in lowered IR
+ */
+
+#include "touch_extractor.h"
+
+#include <set>
+#include <algorithm>
+#include <cmath>
+
+namespace tvm {
+namespace autotvm {
+
+int ParallelLevel(AnnotationType ann) {
+  switch (ann) {
+    case kBlockX: case kBlockY: case kBlockZ:
+      return 2;
+    case kThreadX: case kThreadY: case kThreadZ: case kParallel:
+      return 1;
+    default:
+      return 0;
+  }
+}
+
+// get touch pattern from index expression
+class IndexParser: public IRVisitor {
+ public:
+  void Parse(Expr expr) {
+    pattern_map.clear();
+    this->Visit(expr);
+  }
+
+  void Visit_(const Variable *op) {
+    // TODO(lmzheng): handle more index types (multiple occurrence)
+    if (pattern_map.count(op) == 0) {
+      pattern_map[op] = TouchPattern();
+      pattern_map[op].stride = next_stride_;
+      next_stride_ = 1;
+    }
+  }
+
+  void Visit_(const Mul *op) {
+    if (op->a.as<Variable>()) {
+      if (const auto stride = op->b.as<IntImm>()) {
+        next_stride_ = stride->value;
+      }
+    }
+    IRVisitor::Visit_(op);
+  }
+
+  std::unordered_map<const Variable*, TouchPattern> pattern_map;
+
+ private:
+  int64_t next_stride_ = 1;
+};
+
+// extract iter vars and their touch pattern from ir
+bool TouchExtractor::EnterItervar_(VarExpr var, int64_t length, AnnotationType ann_type) {
+  // do not insert duplicated occurrences of virtual thread
+  if (ann_type == kVirtualThread && itervar_map.count(var) != 0) {
+    skip_stack_size_.push_back(itervar_stack_.size());
+    return true;
+  } else {
+    itervar_stack_.push_back(var);
+    topdown_product_ *= length;
+
+    if (itervar_map.count(var) != 0) {
+      // find two duplicated axes
+      // these happens when we create tvm.thread_axis("threadIdx.x") once and
+      // bind it twice. Here we treat them as two axes
+      // so we create a snapshot for the old one and freeze it
+      VarExpr old = VarExpr(var.get()->name_hint);
+      itervar_map.insert({old, itervar_map[var]});
+      itervar_map.erase(var);
+    }
+
+    itervar_map.insert({var, ItervarFeature(var, length,
+                                            static_cast<int>(itervar_stack_.size()),
+                                            ann_type,
+                                            topdown_product_,
+                                            static_cast<int>(itervar_counter_++))});
+  }
+
+  return true;
+}
+
+void TouchExtractor::ExitItervar_() {
+  if (!skip_stack_size_.empty() && skip_stack_size_.back() == itervar_stack_.size()) {
+    skip_stack_size_.pop_back();
+    return;
+  }
+  VarExpr var = itervar_stack_.back();
+
+  // update count and reuse ratio for upper iter vars (includes self)
+  for (auto kv : itervar_map[var].touch_feature) {
+    if (kv.second.stride != 0) {  // multiply count
+      for (auto stack_var : itervar_stack_) {
+        auto touch_pattern = itervar_map[stack_var].touch_feature.find(kv.first);
+        CHECK(touch_pattern != itervar_map[stack_var].touch_feature.end());
+        touch_pattern->second.count *= itervar_map[var].length;
+      }
+    } else {                      // multiply reuse ratio
+      for (auto stack_var : itervar_stack_) {
+        auto touch_pattern = itervar_map[stack_var].touch_feature.find(kv.first);
+        CHECK(touch_pattern != itervar_map[stack_var].touch_feature.end());
+        touch_pattern->second.reuse *= itervar_map[var].length;
+      }
+    }
+  }
+  itervar_stack_.pop_back();
+
+  topdown_product_ /= itervar_map[var].length;
+  int64_t bottomup_product = -1;
+  for (auto kv : itervar_map[var].touch_feature) {
+    bottomup_product = std::max(bottomup_product, kv.second.count * kv.second.reuse);
+  }
+
+  itervar_map[var].bottomup_product = bottomup_product;
+
+  // push base to upper parallel axis
+  int para_level = ParallelLevel(itervar_map[var].ann);
+  // if is the separate line of parallel level, push the base to upper parallel level
+  if (!itervar_stack_.empty() &&
+      ParallelLevel(itervar_map[itervar_stack_.back()].ann) == para_level + 1) {
+    for (auto kv : itervar_map[var].touch_feature) {
+      for (auto stack_var : itervar_stack_) {
+        if (ParallelLevel(itervar_map[stack_var].ann) == para_level + 1) {
+          auto touch_pattern = itervar_map[stack_var].touch_feature.find(kv.first);
+          CHECK(touch_pattern != itervar_map[stack_var].touch_feature.end());
+          touch_pattern->second.thread_reuse = -kv.second.reuse;
+          touch_pattern->second.thread_count = -kv.second.count;
+          // NOTE: use minus as a flag to denote it is a base,
+          // indicating it is not the final value
+        }
+      }
+    }
+  }
+
+  for (auto kv : itervar_map[var].touch_feature) {
+    if (kv.second.thread_count < 0) {
+      itervar_map[var].touch_feature[kv.first].thread_count =
+          kv.second.count / (-kv.second.thread_count);
+      itervar_map[var].touch_feature[kv.first].thread_reuse =
+          kv.second.reuse / (-kv.second.thread_reuse);
+    }
+  }
+}
+
+void TouchExtractor::EnterMem_(VarExpr buffer_var, Expr index) {
+  std::string name = buffer_var.get()->name_hint;
+  TouchedBuffer buf = name + "_" + std::to_string(buffer_counter_[name]++);
+
+  // extract touch pattern from index
+  IndexParser parser;
+  parser.Parse(index);
+
+  // push up mem access info
+  for (auto var : itervar_stack_) {
+    auto x = parser.pattern_map.find(var.get());
+    if (x != parser.pattern_map.end()) {
+      itervar_map[var].touch_feature[buf] = x->second;
+    } else {
+      itervar_map[var].touch_feature[buf] = TouchPattern();
+    }
+  }
+}
+
+void TouchExtractor::ExitMem_() {
+}
+
+/*!
+ * \brief Get axis-based feature for all axes
+ * \param stmt The statement to be extracted
+ * \param bool Whether take log for numerical feature
+ * \param ret_feature The buffer where the return value is stored
+ *
+ * \note The format of return value is
+ * ((
+ *   ('_itervar_',  var),
+ *   ('_attr_',     length, nest_level, topdown, bottomup, one_hot_annotation),
+ *   ('_arith_',    add_ct, mul_ct, div_ct),
+ *   ('data_vec_0', stride, mod, count, reuse, thread_count, thread_reuse),
+ *   ('conv_0',     stride, mod, count, reuse, thread_count, thread_reuse),
+ * ),
+ * (
+ *   ('_itervar_',    var2),
+ *   ('_attr_',       length, nest_level, one_hot_annotation),
+ *   ('_arith_',      add_ct, mul_ct, div_ct),
+ *   ('kernel_vec_0', stride, mod, count, reuse, thread_count, thread_reuse),
+ *   ('conv_1',       stride, mod, count, reuse, thread_count, thread_reuse),
+ * ))
+ *
+ * Itervars are sorted according to their first occurrence position in IR.
+ * Buffers touched by an itervar are sorted by their unique names.
+ *
+ * \note If you want to flatten these features as the input of your model,
+ * You can use the faster one GetItervarFeatureFlatten below.
+ */
+void GetItervarFeature(Stmt stmt, bool take_log, Array<Array<Array<Expr> > > *ret_feature) {
+  // extract
+  TouchExtractor touch_analyzer;
+  touch_analyzer.Analyze(stmt);
+
+  // sort according to order
+  std::vector<VarExpr> vars;
+  for (auto kv : touch_analyzer.itervar_map) {
+    vars.push_back(kv.first);
+  }
+  std::sort(vars.begin(), vars.end(), [&](const VarExpr &lhs, const VarExpr &rhs) -> bool {
+    return touch_analyzer.itervar_map[lhs].order < touch_analyzer.itervar_map[rhs].order;
+  });
+
+  // whether take log for numerical feature
+  std::function<double(int64_t)> trans;
+  if (take_log) {
+    trans = [](int64_t x) {
+      if (x < 0)
+        return -std::log(-x+1) / std::log(2);
+      x = x + 1;
+      return std::log(x) / std::log(2);
+    };
+  } else {
+    trans = [](int64_t x) {
+      return x;
+    };
+  }
+
+  // serialize for front end
+  for (auto var : vars) {
+    Array<Array<Expr> > feature_row;
+    ItervarFeature &fea = touch_analyzer.itervar_map[var];
+    feature_row.push_back(Array<Expr>{std::string("_itervar_"), var});
+
+    Array<Expr> attr{std::string("_attr_"),
+                     FloatImm::make(Float(32), trans(fea.length)),
+                     IntImm::make(Int(32), fea.nest_level),
+                     FloatImm::make(Float(32), trans(fea.topdown_product)),
+                     FloatImm::make(Float(32), trans(fea.bottomup_product)),
+    };
+    // one hot annotation
+    for (int i = 0; i < kNum; i++) {
+      attr.push_back(i == fea.ann);
+    }
+    feature_row.push_back(attr);
+
+    // arithmetic
+    feature_row.push_back(Array<Expr>{std::string("_arith_"),
+                                      FloatImm::make(Float(32), trans(fea.add_ct)),
+                                      FloatImm::make(Float(32), trans(fea.mul_ct)),
+                                      FloatImm::make(Float(32), trans(fea.div_ct)),
+    });
+
+    // touch map
+    std::vector<TouchedBuffer> bufs;
+    for (auto kv : fea.touch_feature) {
+      bufs.push_back(kv.first);
+    }
+    std::sort(bufs.begin(), bufs.end());
+    for (auto k : bufs) {
+      TouchPattern &v = fea.touch_feature[k];
+      feature_row.push_back(Array<Expr>{k,
+                                        FloatImm::make(Float(32), trans(v.stride)),
+                                        FloatImm::make(Float(32), trans(v.mod)),
+                                        FloatImm::make(Float(32), trans(v.count)),
+                                        FloatImm::make(Float(32), trans(v.reuse)),
+                                        FloatImm::make(Float(32), trans(v.thread_count)),
+                                        FloatImm::make(Float(32), trans(v.thread_reuse)),
+      });
+    }
+
+    ret_feature->push_back(feature_row);
+  }
+}
+
+/*!
+ * \brief Get axis-based feature for all axes and flatten them into a one-dimensional vector.
+ * \param stmt The statement to be extracted
+ * \param bool Whether take log for numerical feature
+ * \param ret_feature The buffer where the return value is stored
+ *
+ * \note See GetItervarFeature for more details about the return value.
+ *       This is an optimized version of GetItervarFeature + Flatten. This runs much faster.
+ */
+void GetItervarFeatureFlatten(Stmt stmt, bool take_log, std::vector<float> *ret_feature) {
+  // extract touch feature
+  TouchExtractor touch_analyzer;
+  touch_analyzer.Analyze(stmt);
+
+  // sort according to order
+  std::vector<VarExpr> vars;
+  for (auto kv : touch_analyzer.itervar_map) {
+    vars.push_back(kv.first);
+  }
+  std::sort(vars.begin(), vars.end(), [&](const VarExpr &lhs, const VarExpr &rhs) -> bool {
+    return touch_analyzer.itervar_map[lhs].order < touch_analyzer.itervar_map[rhs].order;
+  });
+
+  // whether take log for numerical feature
+  std::function<float(int64_t)> trans;
+  if (take_log) {
+    trans = [](int64_t x) {
+      if (x < 0)
+        return -std::log(-x+1) / std::log(2);
+      x = x + 1;
+      return std::log(x) / std::log(2);
+    };
+  } else {
+    trans = [](int64_t x) {
+      return x;
+    };
+  }
+
+  // serialize for front end
+  for (auto var : vars) {
+    ItervarFeature &fea = touch_analyzer.itervar_map[var];
+
+    ret_feature->push_back(trans(fea.length));
+    ret_feature->push_back(fea.nest_level);
+    ret_feature->push_back(trans(fea.topdown_product));
+    ret_feature->push_back(trans(fea.bottomup_product));
+
+    // one hot annotation
+    for (int i = 0; i < kNum; i++) {
+      ret_feature->push_back(i == fea.ann);
+    }
+
+    // arithmetic
+    ret_feature->push_back(trans(fea.add_ct));
+    ret_feature->push_back(trans(fea.mul_ct));
+    ret_feature->push_back(trans(fea.div_ct));
+
+    // touch map
+    std::vector<TouchedBuffer> bufs;
+    for (auto kv : fea.touch_feature) {
+      bufs.push_back(kv.first);
+    }
+    std::sort(bufs.begin(), bufs.end());
+    for (auto k : bufs) {
+      TouchPattern &v = fea.touch_feature[k];
+      ret_feature->push_back(trans(v.stride));
+      ret_feature->push_back(trans(v.mod));
+      ret_feature->push_back(trans(v.count));
+      ret_feature->push_back(trans(v.reuse));
+      ret_feature->push_back(trans(v.thread_count));
+      ret_feature->push_back(trans(v.thread_reuse));
+    }
+  }
+}
+
+/*!
+ * \brief Get curve sample feature (relation feature) and flatten them into a one-dimensional vector.
+ * \param stmt The statement to be extracted
+ * \param sample_n The number of points used for sampling a curve (along one dimension)
+ * \param ret_feature The buffer where the return value is stored
+ */
+void GetCurveSampleFeatureFlatten(Stmt stmt, int sample_n, std::vector<float> *ret_feature) {
+  // extract touch feature
+  TouchExtractor touch_ext;
+  touch_ext.Analyze(stmt);
+
+  // sort according to order
+  std::vector<VarExpr> vars;
+  for (auto kv : touch_ext.itervar_map) {
+    vars.push_back(kv.first);
+  }
+  std::sort(vars.begin(), vars.end(), [&](const VarExpr &lhs, const VarExpr &rhs) -> bool {
+    return touch_ext.itervar_map[lhs].order < touch_ext.itervar_map[rhs].order;
+  });
+
+  int max_depth = 0;
+  std::map<TouchedBuffer, std::vector<double> > reuse_curve;
+  std::map<TouchedBuffer, std::vector<double> > count_curve;
+  std::map<TouchedBuffer, std::vector<double> > topdown_curve;
+  std::map<TouchedBuffer, std::vector<double> > bottomup_curve;
+  std::set<TouchedBuffer> innermost_buffers;
+  std::set<std::string> added;
+
+  // find maximum depth of loop nest
+  for (auto var : vars) {
+    ItervarFeature &fea = touch_ext.itervar_map[var];
+    max_depth = std::max(max_depth, fea.nest_level);
+  }
+
+  // mark inner most buffer
+  for (auto iter = vars.rbegin(); iter != vars.rend(); iter++) {
+    auto var = *iter;
+    ItervarFeature &fea = touch_ext.itervar_map[var];
+    if (fea.nest_level == max_depth) {
+      for (auto kv : fea.touch_feature) {
+        // delete buffer no (e.g. 'A_0' -> 'A', 'A_1' -> 'A')
+        std::string raw_name = kv.first.substr(0, kv.first.rfind("_"));
+
+        // delete memory scope (e.g. 'A.local' -> 'A', 'A.shared' -> 'A')
+        size_t pos = raw_name.find(".");
+        if (pos < kv.first.size())
+          raw_name = raw_name.substr(0, pos);
+
+        // If there are multiple innermost buffers that are derived from a same raw buffer
+        // We only record the last occurrence (note the `iter` is in reverse order)
+        // e.g. `A.local`, `A.shared` are derived from `A`, if they all occurred at the inner most
+        // level, we will only record the last occurrence,
+        if (added.find(raw_name) == added.end()) {
+          innermost_buffers.insert(kv.first);
+          added.insert(raw_name);
+        }
+      }
+    }
+  }
+
+  // pad the first point (zero) for all curves
+  for (auto buf : innermost_buffers) {
+    reuse_curve[buf].push_back(0);
+    count_curve[buf].push_back(0);
+    topdown_curve[buf].push_back(0);
+    bottomup_curve[buf].push_back(0);
+  }
+
+  // extract curves
+  for (auto var : vars) {
+    ItervarFeature &fea = touch_ext.itervar_map[var];
+    for (auto kv : fea.touch_feature) {
+      if (innermost_buffers.find(kv.first) != innermost_buffers.end()) {
+        reuse_curve[kv.first].emplace_back(std::log(kv.second.reuse) / std::log(2));
+        count_curve[kv.first].emplace_back(std::log(kv.second.count) / std::log(2));
+        topdown_curve[kv.first].emplace_back(std::log(fea.topdown_product) / std::log(2));
+        bottomup_curve[kv.first].emplace_back(std::log(fea.bottomup_product) / std::log(2));
+      }
+    }
+  }
+
+  // sample relation in the curve
+  auto sample_curve = [&](const std::vector<double> &x, const std::vector<double> &y,
+                          double weight) {
+    for (int i = 0; i < sample_n; i++) {
+      double xx = i * weight;
+      for (int j = static_cast<int>(x.size()) - 1; j >= 0; j--) {
+        if (xx > x[j] - 1e-6) {
+          ret_feature->emplace_back(y[j]);
+          ret_feature->emplace_back(xx - x[j]);
+          break;
+        }
+      }
+    }
+  };
+
+  // serialize to frontend
+  for (auto k : innermost_buffers) {
+    std::vector<double> &count = count_curve[k];
+    std::vector<double> &reuse = reuse_curve[k];
+    std::vector<double> &top_down = topdown_curve[k];
+
+    std::sort(count.begin(), count.end());
+    std::sort(reuse.begin(), reuse.end());
+    std::sort(top_down.begin(), top_down.end());
+
+    sample_curve(count, reuse, 1);
+    sample_curve(reuse, count, 1);
+    sample_curve(count, top_down, 1);
+    sample_curve(top_down, count, 1);
+  }
+}
+
+
+// register API for front end
+TVM_REGISTER_API("autotvm.feature.GetItervarFeature")
+.set_body([](TVMArgs args, TVMRetValue *ret) {
+  Stmt stmt = args[0];
+  bool take_log = args[1];
+  Array<Array<Array<Expr > > > ret_feature;
+
+  GetItervarFeature(stmt, take_log, &ret_feature);
+
+  *ret = ret_feature;
+});
+
+
+TVM_REGISTER_API("autotvm.feature.GetItervarFeatureFlatten")
+.set_body([](TVMArgs args, TVMRetValue *ret) {
+  Stmt stmt = args[0];
+  bool take_log = args[1];
+  std::vector<float> ret_feature;
+
+  GetItervarFeatureFlatten(stmt, take_log, &ret_feature);
+
+  TVMByteArray arr;
+  arr.size = sizeof(float) * ret_feature.size();
+  arr.data = reinterpret_cast<char *>(ret_feature.data());
+  *ret = arr;
+});
+
+
+TVM_REGISTER_API("autotvm.feature.GetCurveSampleFeatureFlatten")
+.set_body([](TVMArgs args, TVMRetValue *ret) {
+  Stmt stmt = args[0];
+  bool take_log = args[1];
+  std::vector<float> ret_feature;
+
+  GetCurveSampleFeatureFlatten(stmt, take_log, &ret_feature);
+
+  TVMByteArray arr;
+  arr.size = sizeof(float) * ret_feature.size();
+  arr.data = reinterpret_cast<char *>(ret_feature.data());
+  *ret = arr;
+});
+
+
+}  // namespace autotvm
+}  // namespace tvm

src/autotvm/touch_extractor.h

+/*!
+ *  Copyright (c) 2018 by Contributors
+ * \file touch_extractor.h
+ * \brief Extract feature of touch pattern of axes in lowered IR
+ */
+
+#ifndef TVM_AUTOTVM_TOUCH_EXTRACTOR_H_
+#define TVM_AUTOTVM_TOUCH_EXTRACTOR_H_
+
+#include <tvm/ir.h>
+#include <tvm/ir_visitor.h>
+#include <tvm/api_registry.h>
+#include <stack>
+#include <vector>
+#include <map>
+#include <string>
+#include <deque>
+#include "feature_visitor.h"
+
+namespace tvm {
+namespace autotvm {
+
+using TouchedBuffer = std::string;
+
+// touch pattern buf[(stride * var) % mod) + other]
+struct TouchPattern {
+  int64_t stride{0};
+  int64_t mod{-1};  // -1 for +inf
+
+  int64_t count{1};
+  int64_t reuse{1};
+  int64_t thread_count{0};  // count when move thread axis into innermost
+  int64_t thread_reuse{0};  // reuse ratio move thread axis into innermost
+};
+
+// all the feature of an iter var
+struct ItervarFeature {
+  ItervarFeature(VarExpr var,
+                 int64_t extent,
+                 int nest,
+                 AnnotationType ann_type,
+                 int64_t topdown,
+                 int counter)
+      : length(extent), nest_level(nest), ann(ann_type), topdown_product(topdown), order(counter) {}
+  ItervarFeature() {}
+
+  // Axis Attributes
+  int64_t length;
+  int nest_level;
+  AnnotationType ann;         // one-hot axis type
+  int64_t topdown_product;    // accumulative product of axis length, in top-down order
+  int64_t bottomup_product;   // accumulative product of axis length, in bottom-up order
+  // bottomup_product = reuse * count for any touched buffer
+
+  int order;  // used for soring axis
+
+  // Arithmetic feature
+  int add_ct{0};
+  int mul_ct{0};
+  int div_ct{0};
+
+  // Memory Touch Feature
+  std::unordered_map<TouchedBuffer, TouchPattern> touch_feature;
+};
+
+// extract iter vars and their touch pattern from ir
+class TouchExtractor : public FeatureVisitor {
+ public:
+  void Analyze(Stmt stmt) {
+    this->Visit(stmt);
+  }
+
+  // arithmetic stats
+  void Visit_(const Add *op) {
+    if (op->type.is_float())
+      itervar_map[itervar_stack_.back()].add_ct++;
+    IRVisitor::Visit_(op);
+  }
+
+  void Visit_(const Sub *op) {
+    if (op->type.is_float())
+      itervar_map[itervar_stack_.back()].add_ct++;
+    IRVisitor::Visit_(op);
+  }
+
+  void Visit_(const Mul *op) {
+    if (op->type.is_float())
+      itervar_map[itervar_stack_.back()].mul_ct++;
+    IRVisitor::Visit_(op);
+  }
+
+  void Visit_(const Div *op) {
+    if (op->type.is_float())
+      itervar_map[itervar_stack_.back()].div_ct++;
+    IRVisitor::Visit_(op);
+  }
+
+  void Visit_(const Mod *op) {
+    if (op->type.is_float())
+      itervar_map[itervar_stack_.back()].div_ct++;
+    IRVisitor::Visit_(op);
+  }
+
+  std::unordered_map<VarExpr, ItervarFeature, tvm::ExprHash, tvm::ExprEqual> itervar_map;
+
+ private:
+  bool EnterItervar_(VarExpr var, int64_t length, AnnotationType ann_type);
+  void ExitItervar_();
+  void EnterMem_(VarExpr buffer_var, Expr index);
+  void ExitMem_();
+
+  int64_t topdown_product_{1};
+  std::map<std::string, size_t> buffer_counter_;
+  size_t itervar_counter_{0};
+  std::deque<VarExpr> itervar_stack_;  // use deque instead of stack for indexing
+  std::deque<size_t> skip_stack_size_;
+
+  using IRVisitor::Visit_;
+};
+
+}  // namespace autotvm
+}  // namespace tvm
+
+#endif  // TVM_AUTOTVM_TOUCH_EXTRACTOR_H_

src/codegen/build_module.cc

@@ -73,7 +73,7 @@ Target CreateTarget(const std::string& target_name,
     } else {
       t->device_type = kDLROCM;
     }
-    t->keys_array.push_back(ir::StringImm::make("rocm"));
+    t->keys_array.push_back(ir::StringImm::make(target_name));
     t->keys_array.push_back(ir::StringImm::make("gpu"));
     t->max_num_threads = 256;
     if (t->device_name == "intel_graphics") {
@@ -195,11 +195,7 @@ Target Target::create(const std::string& target_str) {
     options.push_back(item);
   }
 
-  if (device_name == "rasp") {
-    return target::rasp(options);
-  } else {
   return CreateTarget(target_name, options);
-  }
 }
 
 /*! \brief Entry to hold the Target context stack. */

src/pass/verify_gpu_code.cc

@@ -18,13 +18,13 @@ class GPUCodeVerifier : public IRVisitor {
   bool Verify(tvm::Stmt stmt,
               int64_t max_local_memory_per_block,
               int64_t max_shared_memory_per_block,
-              int64_t max_thread_per_block,
+              int64_t max_threads_per_block,
               int64_t max_thread_x,
               int64_t max_thread_y,
               int64_t max_thread_z) {
     max_local_memory_per_block_ = static_cast<size_t>(max_local_memory_per_block);
     max_shared_memory_per_block_ = static_cast<size_t>(max_shared_memory_per_block);
-    max_thread_per_block_ = static_cast<size_t>(max_thread_per_block);
+    max_threads_per_block_ = static_cast<size_t>(max_threads_per_block);
     max_thread_x_ = static_cast<size_t>(max_thread_x);
     max_thread_y_ = static_cast<size_t>(max_thread_y);
     max_thread_z_ = static_cast<size_t>(max_thread_z);
@@ -52,7 +52,7 @@ class GPUCodeVerifier : public IRVisitor {
 
     if (nest_level_ == 0) {
       // exit a kernel, check the validity
-      valid_ &= thread_per_block_ <= max_thread_per_block_;
+      valid_ &= thread_per_block_ <= max_threads_per_block_;
 
       valid_ &= local_memory_per_block_ <= max_local_memory_per_block_;
       valid_ &= shared_memory_per_block_ <= max_shared_memory_per_block_;
@@ -117,7 +117,7 @@ class GPUCodeVerifier : public IRVisitor {
 
   size_t max_local_memory_per_block_;
   size_t max_shared_memory_per_block_;
-  size_t max_thread_per_block_;
+  size_t max_threads_per_block_;
   size_t max_thread_x_, max_thread_y_, max_thread_z_;
 
   bool valid_{true};
@@ -137,26 +137,34 @@ bool VerifyGPUCode(Stmt stmt,
                    Map<std::string, Expr> constraints) {
   GPUCodeVerifier verifier;
 
-  auto get_int = [&constraints](std::string key, int64_t def) {
-    auto iter = constraints.find(key);
-    if (iter != constraints.end()) {
-      return ((*iter).second).as<IntImm>()->value;
-    } else {
-      return def;
+  int64_t max_local_memory_per_block = INT64_MAX;
+  int64_t max_shared_memory_per_block = INT64_MAX;
+  int64_t max_threads_per_block = INT64_MAX;
+  int64_t max_thread_x = INT64_MAX;
+  int64_t max_thread_y = INT64_MAX;
+  int64_t max_thread_z = INT64_MAX;
+
+  for (auto iter : constraints) {
+    if (iter.first == "max_local_memory_per_block")
+      max_local_memory_per_block = (iter.second).as<IntImm>()->value;
+    else if (iter.first == "max_shared_memory_per_block")
+      max_shared_memory_per_block = (iter.second).as<IntImm>()->value;
+    else if (iter.first == "max_threads_per_block")
+      max_threads_per_block = (iter.second).as<IntImm>()->value;
+    else if (iter.first == "max_thread_x")
+      max_thread_x = (iter.second).as<IntImm>()->value;
+    else if (iter.first == "max_thread_y")
+      max_thread_y = (iter.second).as<IntImm>()->value;
+    else if (iter.first == "max_thread_z")
+      max_thread_z = (iter.second).as<IntImm>()->value;
+    else
+      LOG(FATAL) << "Invalid check item: " << iter.first;
   }
-  };
-
-  int64_t max_local_memory_per_block = get_int("max_local_memory_per_block", INT64_MAX);
-  int64_t max_shared_memory_per_block = get_int("max_shared_memory_per_block", INT64_MAX);
-  int64_t max_thread_per_block = get_int("max_thread_per_block", INT64_MAX);
-  int64_t max_thread_x = get_int("max_thread_x", INT64_MAX);
-  int64_t max_thread_y = get_int("max_thread_y", INT64_MAX);
-  int64_t max_thread_z = get_int("max_thread_z", INT64_MAX);
 
   return verifier.Verify(stmt,
                          max_local_memory_per_block,
                          max_shared_memory_per_block,
-                         max_thread_per_block,
+                         max_threads_per_block,
                          max_thread_x,
                          max_thread_y,
                          max_thread_z);

tests/python/integration/test_tuning.py

+"""
+Test the tuner
+"""
+import logging
+import time
+
+import tvm
+
+from tvm import autotvm
+from tvm.autotvm.tuner import RandomTuner
+
+@autotvm.template
+def conv2d_no_batching(N, H, W, CI, CO, KH, KW):
+    """An example template for testing"""
+    assert N == 1, "Only consider batch_size = 1 in this template"
+
+    data = tvm.placeholder((N, CI, H, W), name='data')
+    kernel = tvm.placeholder((CO, CI, KH, KW), name='kernel')
+
+    rc = tvm.reduce_axis((0, CI), name='rc')
+    ry = tvm.reduce_axis((0, KH), name='ry')
+    rx = tvm.reduce_axis((0, KW), name='rx')
+
+    conv = tvm.compute(
+        (N, CO, H - KH + 1, W - KW + 1),
+        lambda nn, ff, yy, xx: tvm.sum(
+            data[nn, rc, yy + ry, xx + rx] * kernel[ff, rc, ry, rx],
+            axis=[rc, ry, rx]), tag="conv2d_nchw")
+
+    s = tvm.create_schedule([conv.op])
+
+    output = conv
+    OL = s.cache_write(conv, 'local')
+
+    # create cache stage
+    AA = s.cache_read(data, 'shared', [OL])
+    WW = s.cache_read(kernel, 'shared', [OL])
+    AL = s.cache_read(AA, 'local', [OL])
+    WL = s.cache_read(WW, 'local', [OL])
+
+    # tile and bind spatial axes
+    n, f, y, x = s[output].op.axis
+    cfg = autotvm.get_config()
+    cfg.define_split("tile_f", cfg.axis(f), num_outputs=4)
+    cfg.define_split("tile_y", cfg.axis(y), num_outputs=4)
+    cfg.define_split("tile_x", cfg.axis(x), num_outputs=4)
+    bf, vf, tf, fi = cfg["tile_f"].apply(s, output, f)
+    by, vy, ty, yi = cfg["tile_y"].apply(s, output, y)
+    bx, vx, tx, xi = cfg["tile_x"].apply(s, output, x)
+    kernel_scope = n  # this is the scope to attach global config inside this kernel
+
+    s[output].bind(bf, tvm.thread_axis("blockIdx.z"))
+    s[output].bind(by, tvm.thread_axis("blockIdx.y"))
+    s[output].bind(bx, tvm.thread_axis("blockIdx.x"))
+    s[output].bind(vf, tvm.thread_axis("vthread"))
+    s[output].bind(vy, tvm.thread_axis("vthread"))
+    s[output].bind(vx, tvm.thread_axis("vthread"))
+    s[output].bind(tf, tvm.thread_axis("threadIdx.z"))
+    s[output].bind(ty, tvm.thread_axis("threadIdx.y"))
+    s[output].bind(tx, tvm.thread_axis("threadIdx.x"))
+    s[output].reorder(n, bf, by, bx, vf, vy, vx, tf, ty, tx, fi, yi, xi)
+    s[OL].compute_at(s[output], tx)
+
+    # tile and bind reduction axes
+    n, f, y, x = s[OL].op.axis
+    rc, ry, rx = s[OL].op.reduce_axis
+    cfg.define_split("tile_rc", cfg.axis(rc), num_outputs=3)
+    cfg.define_split("tile_ry", cfg.axis(ry), num_outputs=3)
+    cfg.define_split("tile_rx", cfg.axis(rx), num_outputs=3)
+    rco, rcm, rci = cfg['tile_rc'].apply(s, OL, rc)
+    ryo, rym, ryi = cfg['tile_rx'].apply(s, OL, ry)
+    rxo, rxm, rxi = cfg['tile_ry'].apply(s, OL, rx)
+    s[OL].reorder(rco, ryo, rxo, rcm, rym, rxm, rci, ryi, rxi, n, f, y, x)
+
+    s[AA].compute_at(s[OL], rxo)
+    s[WW].compute_at(s[OL], rxo)
+    s[AL].compute_at(s[OL], rxm)
+    s[WL].compute_at(s[OL], rxm)
+
+    # cooperative fetching
+    for load in [AA, WW]:
+        n, f, y, x = s[load].op.axis
+        fused = s[load].fuse(n, f, y, x)
+        tz, fused = s[load].split(fused, nparts=cfg["tile_f"].size[2])
+        ty, fused = s[load].split(fused, nparts=cfg["tile_y"].size[2])
+        tx, fused = s[load].split(fused, nparts=cfg["tile_x"].size[2])
+        s[load].bind(tz, tvm.thread_axis("threadIdx.z"))
+        s[load].bind(ty, tvm.thread_axis("threadIdx.y"))
+        s[load].bind(tx, tvm.thread_axis("threadIdx.x"))
+
+    # tune unroll
+    cfg.define_knob("auto_unroll_max_step", [0, 512, 1500])
+    cfg.define_knob("unroll_explicit", [0, 1])
+    s[output].pragma(kernel_scope, 'auto_unroll_max_step', cfg['auto_unroll_max_step'].val)
+    s[output].pragma(kernel_scope, 'unroll_explicit', cfg['unroll_explicit'].val)
+
+    return s, [data, kernel, conv]
+
+def get_sample_task(target=tvm.target.cuda(), target_host=None):
+    """return a sample task for testing"""
+    task = autotvm.task.create(conv2d_no_batching,
+                               args=(1, 7, 7, 512, 512, 3, 3),
+                               target=target, target_host=target_host)
+    return task, target
+
+
+def test_task_tuner_without_measurement():
+    """test task and tuner without measurement"""
+    task, target = get_sample_task()
+
+    def measure_batch(inputs):
+        from tvm.autotvm import MeasureResult
+
+        results = []
+        for inp in inputs:
+            tic = time.time()
+            # do nothing
+            time.sleep(0.001)
+            results.append(MeasureResult([time.time() - tic], 0,
+                                         time.time() - tic, time.time()))
+        return results
+    measure_option = autotvm.measure_option(mode='custom',
+                                            custom_measure_batch=measure_batch)
+
+    logging.info("%s", task.config_space)
+
+    # new tuner and recorder
+    for tuner_class in [autotvm.tuner.RandomTuner, autotvm.tuner.GridSearchTuner]:
+        tuner = tuner_class(task)
+        tuner.tune(n_trial=10, measure_option=measure_option)
+
+def test_tuning_with_measure():
+    def check(target, target_host):
+        ctx = tvm.context(target, 0)
+        if not ctx.exist:
+            logging.info("Skip test because %s is not available" % target)
+            return
+
+        # init task
+        task, target = get_sample_task(target, target_host)
+        logging.info("%s", task.config_space)
+
+        measure_option = autotvm.measure_option(mode='local',
+                                                timeout=4,
+                                                number=2)
+
+        tuner = RandomTuner(task)
+        tuner.tune(n_trial=10, measure_option=measure_option)
+
+    check("cuda", None)
+    check("opencl", None)
+
+if __name__ == "__main__":
+    # only print log when invoked from main
+    logging.basicConfig(level=logging.INFO)
+
+    test_task_tuner_without_measurement()
+    test_tuning_with_measure()

tests/python/unittest/test_autotvm_common.py

+"""Common utilities for testing autotvm"""
+import time
+
+import tvm
+from tvm import autotvm
+from tvm.autotvm import MeasureInput, MeasureResult
+
+@autotvm.template
+def matmul(N, L, M, dtype):
+    A = tvm.placeholder((N, L), name='A', dtype=dtype)
+    B = tvm.placeholder((L, M), name='B', dtype=dtype)
+
+    k = tvm.reduce_axis((0, L), name='k')
+    C = tvm.compute((N, M), lambda i, j: tvm.sum(A[i, k] * B[k, j], axis=k), name='C')
+    s = tvm.create_schedule(C.op)
+
+    # schedule
+    y, x = s[C].op.axis
+    k = s[C].op.reduce_axis[0]
+
+    ##### define space begin #####
+    cfg = autotvm.get_config()
+    cfg.define_split("tile_y", y, num_outputs=2)
+    cfg.define_split("tile_x", x, num_outputs=2)
+    ##### define space end #####
+
+    # schedule according to config
+    yo, yi = cfg["tile_y"].apply(s, C, y)
+    xo, xi = cfg["tile_x"].apply(s, C, x)
+
+    s[C].reorder(yo, xo, k, yi, xi)
+
+    return s, [A, B, C]
+
+def get_sample_task(n=128):
+    """return a sample task for testing"""
+    target = tvm.target.create("llvm")
+    task = autotvm.task.create(matmul, args=(n, n, n, 'float32'), target=target)
+    return task, target
+
+def get_sample_records(n):
+    """get sample records for testing"""
+    tsk, target = get_sample_task()
+
+    inps, ress = [], []
+    for i in range(n):
+        inps.append(MeasureInput(target, tsk, tsk.config_space.get(i)))
+        ress.append(MeasureResult((i+1,), 0, i, time.time()))
+    return list(zip(inps, ress))
+

tests/python/unittest/test_autotvm_database.py

+"""Test database"""
+import copy
+import logging
+import time
+
+import numpy as np
+import tvm
+
+from tvm import autotvm
+from tvm.autotvm import database
+from tvm.autotvm.measure.measure_methods import HashMismatchError
+from tvm.autotvm.record import encode, MeasureInput, MeasureResult
+
+from test_autotvm_common import get_sample_task, get_sample_records
+
+def test_save_load():
+    logging.info("test basic db load/save ...")
+    records = get_sample_records(3)
+    inp1, res1 = records[0]
+    inp2, res2 = records[1]
+    inp3, _ = records[2]
+
+    _db = database.DummyDatabase()
+    _db.flush()
+    _db.save(inp1, res1)
+    _db.save(inp2, res2)
+
+    load1 = _db.load(inp1)
+    load2 = _db.load(inp2)
+    load3 = _db.load(inp3)
+    assert load1 == res1
+    assert load2 == res2
+    assert load3 is None
+    assert load1 != load2
+
+TRIAL_LIMIT = 2
+
+def test_db_filter():
+    logging.info("test db filter ...")
+
+    # Pick a GPU target because there are more likely to be failures/invalid configs
+    task, target = get_sample_task()
+
+    ctx = tvm.context(str(target))
+    if not ctx.exist:
+        logging.warning("Skip this test because there is no supported device for test")
+
+    batch_size = 2
+
+    measure_option = autotvm.measure_option(mode='local-nofork', timeout=2)
+    measure_batch = autotvm.measure.create_measure_batch(task, measure_option)
+
+    ct = 0
+    all_inputs = list()
+    all_results = list()
+    batches = list()
+    tuner = autotvm.tuner.RandomTuner(task)
+    while ct < TRIAL_LIMIT:
+        inputs = list()
+        for i in range(batch_size):
+            cfg = tuner.next_batch(1)[0]
+            inputs.append((MeasureInput(target, task, cfg)))
+            all_inputs.append(inputs[-1])
+        batches.append(inputs)
+        results = measure_batch(inputs)
+        all_results += results
+        ct += 1
+
+    del measure_batch
+
+    db = database.DummyDatabase()
+    db.flush()
+
+    # First setting, memoize one input at a time, check that each is saved and replayed
+    measure_option = autotvm.measure_option(mode='local-nofork', timeout=2, replay_db=db)
+    measure_batch = autotvm.measure.create_measure_batch(task, measure_option)
+
+    for i in range(len(all_inputs)+1):
+        db.flush()
+        for j in range(i):
+            db.save(all_inputs[j], all_results[j])
+
+        for k in range(len(batches)):
+            batch = batches[k]
+            batch_result = measure_batch(batch)
+            for l in range(batch_size):
+                all_idx = k*batch_size + l
+                assert batch_result[l] is not None
+                if all_idx < i:
+                    assert encode(batch[l], batch_result[l]) == encode(batch[l], all_results[all_idx]), \
+                        "(no retry) EXPECTED MATCH, GOT MISMATCH"
+                else:
+                    assert encode(batch[l], batch_result[l]) != encode(batch[l], all_results[all_idx]), \
+                        "(no retry) EXPECTED MISMATCH, GOT MATCH"
+
+    del measure_batch
+
+def test_db_hash():
+    logging.info("test db hash check ...")
+    inp1, res1 = get_sample_records(1)[0]
+    inp2 = copy.deepcopy(inp1)
+    inp1.config.code_hash = 'cafecafe'
+    inp2.config.code_hash = 'dbffdbff'
+    res2l = list(tuple(res1))
+
+    # set timestamp
+    res2l[-1] = -1
+    res2 = MeasureResult(*res2l)
+    _db = database.DummyDatabase()
+    _db.flush()
+    _db.save(inp1, res1, extend=True)
+    _db.save(inp2, res2, extend=True)
+
+    load1 = _db.load(inp1)
+    load2 = _db.load(inp2)
+    assert load1 != load2
+    assert load1.timestamp != -1
+    assert load2.timestamp == -1
+
+def test_db_latest_all():
+    logging.info("test db load w/ multiple results ...")
+    inp1, res1 = get_sample_records(1)[0]
+    lis1 = list(tuple(res1))
+    lis2 = list(tuple(res1))
+    lis3 = list(tuple(res1))
+
+    # set timestamp
+    lis1[-1] = 0.0
+    lis2[-1] = 1.1
+    lis3[-1] = 9999.9999
+    res1 = MeasureResult(*lis1)
+    res2 = MeasureResult(*lis2)
+    res3 = MeasureResult(*lis3)
+
+    _db = database.DummyDatabase()
+    _db.flush()
+    _db.save(inp1, res1, extend=True)
+    load1 = _db.load(inp1)
+    assert load1.timestamp == 0.0
+    _db.save(inp1, res2, extend=True)
+    load2 = _db.load(inp1)
+    assert load2.timestamp == 1.1
+    _db.save(inp1, res3, extend=True)
+    load3 = _db.load(inp1)
+    assert load3.timestamp == 9999.9999
+
+    load4 = _db.load(inp1, get_all=True)
+    assert encode(inp1, load4[0]) == encode(inp1, res1)
+    assert encode(inp1, load4[1]) == encode(inp1, res2)
+    assert encode(inp1, load4[2]) == encode(inp1, res3)
+
+def test_db_save_replay():
+    logging.info("test db save (from measure_batch) and replay ...")
+    _db = database.DummyDatabase()
+    _db.flush()
+
+    task, target = get_sample_task()
+
+    ctx = tvm.context(str(target))
+    if not ctx.exist:
+        logging.warning("Skip this test because there is no supported device for test")
+
+    measure_option = autotvm.measure_option(mode='local-nofork',
+                                            timeout=2,
+                                            replay_db=_db, save_to_replay_db=True)
+    measure_batch = autotvm.measure.create_measure_batch(task, measure_option)
+
+    batch_size = 2
+
+    ct = 0
+    all_inputs = list()
+    all_results = list()
+    batches = list()
+    tuner = autotvm.tuner.RandomTuner(task)
+    while ct < TRIAL_LIMIT:
+        inputs = list()
+        for i in range(batch_size):
+            cfg = tuner.next_batch(1)[0]
+            inputs.append((MeasureInput(target, task, cfg)))
+            all_inputs.append(inputs[-1])
+        batches.append(inputs)
+        results = measure_batch(inputs)
+        all_results += results
+        ct += 1
+
+    assert len(_db.db.keys()) == batch_size * TRIAL_LIMIT, \
+        "%d vs %d" % (len(_db.db.keys()), batch_size * TRIAL_LIMIT)
+
+    all_results_2 = measure_batch(all_inputs)
+    all_results_3 = measure_batch(all_inputs)
+
+    for i in range(len(all_results)):
+        encr1 = encode(all_inputs[i], all_results[i])
+        encr2 = encode(all_inputs[i], all_results_2[i])
+        encr3 = encode(all_inputs[i], all_results_3[i])
+        assert encr1 == encr2, "EXPECTED MATCH WITH SAVE REPLAY (first replay), got MISMATCH"
+        assert encr2 == encr3, "EXPECTED MATCH WITH SAVE REPLAY (second replay), got MISMATCH"
+
+    del measure_batch
+
+def test_check_hashmismatch():
+    logging.info("test hash mismatch check")
+
+    task, target = get_sample_task()
+
+    ctx = tvm.context(str(target))
+    if not ctx.exist:
+        logging.warning("Skip this test because there is no supported device for test")
+
+    measure_option = autotvm.measure_option(mode='local-nofork')
+    measure_batch = autotvm.measure.create_measure_batch(task, measure_option)
+
+    inputs = list()
+    cfg = task.config_space.get(np.random.randint(len(task.config_space)))
+    # notvalidh is not a valid CRC32 hash (not hex)
+    cfg.code_hash = 'notvalidh'
+    inputs.append((MeasureInput(target, task, cfg)))
+
+    try:
+        results = measure_batch(inputs)
+        assert False, "HashMismatchError should be raised"
+    except HashMismatchError:
+        pass
+
+    del measure_batch
+
+if __name__ == '__main__':
+    logging.basicConfig(level=logging.INFO)
+    test_save_load()
+    test_db_filter()
+    test_db_hash()
+    test_db_latest_all()
+    test_db_save_replay()
+    test_check_hashmismatch()

tests/python/unittest/test_autotvm_dispatch_context.py

+"""Test dispatcher.
+The dispatcher can choose which template to use according
+to the parameters of workload"""
+
+from collections import namedtuple
+from tvm.autotvm.task import dispatcher, DispatchContext
+
+SimpleWorkload = namedtuple("SimpleWorkload", ["key"])
+SimpleConfig = namedtuple("SimpleConfig", ["template_key"])
+
+def test_dispatch():
+    @dispatcher
+    def my_dispatcher(a, b):
+        return SimpleWorkload(key=a + b)
+
+    @my_dispatcher.register("spatial_pack")
+    def _sp_pack_add(cfg, a, b):
+        return b + 100
+
+    @my_dispatcher.register("im2col")
+    def _im2col_add(cfg, a, b):
+        return a + 1
+
+    class SimpleDispatcher(DispatchContext):
+        def query(self, target, workload):
+            tkey = "spatial_pack" if workload.key > 2 else "im2col"
+            return SimpleConfig(tkey)
+
+    with SimpleDispatcher():
+        # im2col
+        assert my_dispatcher(1, 0) == 2
+        # spack
+        assert my_dispatcher(1, 100) == 200
+
+if __name__ == "__main__":
+    test_dispatch()

tests/python/unittest/test_autotvm_executor.py

+"""Test local executor"""
+import time
+
+from tvm.autotvm.measure import LocalExecutor, executor
+
+def slow(n):
+    r = 0
+    for i in range(0, n+1):
+        r += i
+    return r
+
+def fast(n):
+    return n*(n+1)//2
+
+def test_local_measure_async():
+    ex = LocalExecutor()
+    f1 = ex.submit(slow, 9999999)
+    f2 = ex.submit(fast, 9999999)
+    t1 = 0
+    t2 = 0
+    while True:
+        if t1 == 0 and f1.done():
+            t1 = time.time()
+        if t2 == 0 and f2.done():
+            t2 = time.time()
+        if t1 != 0 and t2 != 0:
+            break
+    assert t2 < t1, "Expected fast async job to finish first!"
+    assert f1.get() == f2.get()
+
+def timeout_job(n):
+    time.sleep(n * 1.5)
+
+def test_timeout():
+    timeout = 0.5
+
+    ex = LocalExecutor(timeout=timeout)
+
+    f1 = ex.submit(timeout_job, timeout)
+    while not f1.done():
+        pass
+    res = f1.get()
+    assert isinstance(res, executor.TimeoutError)
+
+if __name__ == "__main__":
+    test_local_measure_async()
+    test_timeout()

tests/python/unittest/test_autotvm_feature.py

+"""Test feature extraction"""
+
+import numpy as np
+
+import tvm
+from tvm.autotvm import feature
+
+def test_iter_feature_gemm():
+    N = 128
+
+    k = tvm.reduce_axis((0, N), 'k')
+    A = tvm.placeholder((N, N), name='A')
+    B = tvm.placeholder((N, N), name='B')
+    C = tvm.compute(
+        A.shape,
+        lambda y, x: tvm.sum(A[y, k] * B[k, x], axis=k),
+        name='C')
+
+    s = tvm.create_schedule(C.op)
+
+    feas = feature.get_itervar_feature(s, [A, B, C], take_log=False)
+
+    expected = [
+        {
+            '_attr_': [128, 1, 128, 2097152, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
+            'A_0': [128, -1, 16384, 128, 0, 0], 'B_0': [0, -1, 16384, 128, 0, 0],
+            'C_0': [128, -1, 16384, 128, 0, 0], 'C_1': [128, -1, 16384, 128, 0, 0],
+        },
+        {
+            '_attr_': [128, 2, 16384, 16384, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
+            'A_0': [0, -1, 128, 128, 0, 0], 'B_0': [1, -1, 16384, 1, 0, 0],
+            'C_0': [1, -1, 128, 128, 0, 0], 'C_1': [1, -1, 128, 128, 0, 0],
+        },
+        {
+            '_attr_': [128, 3, 2097152, 128, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
+            'A_0': [1, -1, 128, 1, 0, 0], 'B_0': [128, -1, 128, 1, 0, 0],
+            'C_1': [0, -1, 1, 128, 0, 0], 'C_2':  [0, -1, 1, 128, 0, 0],
+        }
+    ]
+
+    for ans, row in zip(expected, feas):
+        for pair in row:
+            if pair[0] not in ans:
+                continue
+            assert ans[pair[0]] == pair[1:], "%s: %s vs %s" % (pair[0], ans[pair[0]], pair[1:])
+
+
+def test_feature_shape():
+    """test the dimensions of flatten feature are the same"""
+
+    N = 1024
+    n_sample = 100
+
+    def get_gemm_feature(target):
+        k = tvm.reduce_axis((0, N), 'k')
+        A = tvm.placeholder((N, N), name='A')
+        B = tvm.placeholder((N, N), name='B')
+        C = tvm.compute(A.shape, lambda y, x: tvm.sum(A[y, k] * B[k, x], axis=k),
+                        name='C')
+
+        s = tvm.create_schedule(C.op)
+
+        y, x = s[C].op.axis
+        axes = list(s[C].tile(y, x, 8, 8)) + [k]
+        perm = np.random.permutation(5)
+        axes = [axes[x] for x in perm]
+        s[C].reorder(*axes)
+
+        if "gpu" in target.keys:
+            pick = []
+            # filter out reduction axis
+            for i in range(len(perm)):
+                if perm[i] != 4:
+                    pick.append(axes[i])
+            s[C].bind(pick[0], tvm.thread_axis("blockIdx.x"))
+            s[C].bind(pick[1], tvm.thread_axis("vthread"))
+            s[C].bind(pick[2], tvm.thread_axis("threadIdx.y"))
+
+        with target:
+            feas = feature.get_itervar_feature(s, [A, B, C])
+            feas = feature.flatten_itervar_feature(feas)
+        return feas
+
+    targets = [
+        tvm.target.cuda(),
+        tvm.target.mali(),
+        tvm.target.rasp(),
+    ]
+
+    for target in targets:
+        dim = len(get_gemm_feature(target))
+        for i in range(n_sample):
+            assert dim == len(get_gemm_feature(target)), "dimensions of feature do not match" \
+                                                   " for different configurations"
+
+
+if __name__ == "__main__":
+    test_iter_feature_gemm()
+    test_feature_shape()

tests/python/unittest/test_autotvm_flop_calculator.py

+"""Test flop calculation"""
+
+import tvm
+import numpy as np
+
+from tvm.autotvm.task.task import compute_flop
+
+def test_conv():
+    for i in range(5):
+        N, H, W, CO, CI, KH, KW = [np.random.randint(10, 32) for _ in range(7)]
+        D = tvm.placeholder((N, CI, H, W))
+        K = tvm.placeholder((CO, CI, KH, KW))
+
+        KH = min(H, KH)
+        KW = min(W, KW)
+
+        ci = tvm.reduce_axis((0, CI))
+        kh = tvm.reduce_axis((0, KH))
+        kw = tvm.reduce_axis((0, KW))
+
+        OH = (H - KH) + 1
+        OW = (W - KW) + 1
+
+        C = tvm.compute((N, CO, OH, OW), lambda n, co, h, w:
+        tvm.sum(D[n][ci][h][w] * K[co][ci][h][w], axis=[ci, kh, kw]))
+
+        s = tvm.create_schedule([C.op])
+
+        assert compute_flop(s) == 2 * N * CO * OH * OW * CI * KH * KW
+
+def test_pack_gemm():
+    for i in range(5):
+        N, L, M = [np.random.randint(10, 128) * 4 for _ in range(3)]
+        A = tvm.placeholder((N, L))
+        B = tvm.placeholder((M, L))
+        k = tvm.reduce_axis((0, L))
+
+        bn = 4
+        A_pack = tvm.compute((N // bn, L, bn), lambda i, j, k: A[i * bn + k][j])
+        B_pack = tvm.compute((M // bn, L, bn), lambda i, j, k: B[i * bn + k][j])
+        C_pack = tvm.compute((N // bn, M // bn, bn, bn), lambda i, j, ii, jj:
+        tvm.sum(A_pack[i, k, ii] * B_pack[j, k, jj], axis=[k]))
+        C = tvm.compute((N, M), lambda i, j: C_pack[i // bn][j // bn][i % bn][j % bn])
+
+        s = tvm.create_schedule([C.op])
+        assert compute_flop(s) == 2 * N * L * M
+
+def test_outer_dot():
+    for i in range(5):
+        N, M = [np.random.randint(10, 128) * 4 for _ in range(2)]
+        A = tvm.placeholder((N,))
+        B = tvm.placeholder((M,))
+
+        C = tvm.compute((N, M), lambda i, j: A[i] * B[j])
+
+        s = tvm.create_schedule([C.op])
+        assert compute_flop(s) == N * M
+
+def test_move():
+    """No float number operation in simple move. So the estimator should raise an error """
+    N = 1024
+
+    A = tvm.placeholder((N,))
+    C = tvm.compute((N,), lambda i: A[i])
+    s = tvm.create_schedule([C.op])
+
+    try:
+        compute_flop(s)
+        assert False
+    except RuntimeError:
+        pass
+
+if __name__ == '__main__':
+    test_conv()
+    test_pack_gemm()
+    test_outer_dot()
+    test_move()

tests/python/unittest/test_autotvm_record.py

+"""test the correctness of dump and load of data log"""
+import time
+
+import tvm
+from tvm.contrib import util
+
+from tvm import autotvm
+from tvm.autotvm.measure import MeasureInput, MeasureResult, MeasureErrorNo
+from tvm.autotvm.record import encode, decode, ApplyHistoryBest, measure_str_key
+
+from test_autotvm_common import get_sample_task
+
+def test_load_dump():
+    task, target = get_sample_task()
+
+    inp = MeasureInput(target, task, task.config_space.get(0))
+    result = MeasureResult((2.0, 2.23, 0.23, 0.123, 0.234, 0.123), MeasureErrorNo.NO_ERROR,
+                           2.3, time.time())
+
+    for protocol in ['json', 'pickle']:
+        row = encode(inp, result, protocol=protocol)
+        inp_2, result_2 = decode(row, protocol=protocol)
+
+        assert measure_str_key(inp) == measure_str_key(inp_2), \
+            "%s vs %s" % (measure_str_key(inp), measure_str_key(inp_2))
+        assert result.costs == result_2.costs
+        assert result.error_no == result_2.error_no
+        assert result.timestamp == result_2.timestamp
+
+
+def test_file_io():
+    temp = util.tempdir()
+    file_path = temp.relpath("temp.log")
+
+    tsk, target = get_sample_task()
+    inputs = [MeasureInput(target, tsk, tsk.config_space.get(i)) for i in range(0, 10)]
+    results = [MeasureResult((i, ), 0, 0, 0) for i in range(0, 10)]
+
+    with open(file_path, "w") as fo:
+        cb = autotvm.callback.log_to_file(fo)
+        cb(None, inputs, results)
+
+    ref = zip(inputs, results)
+    for x, y in zip(ref, autotvm.record.load_from_file(file_path)):
+        assert x[1] == y[1]
+
+
+def test_apply_history_best():
+    tsk, target = get_sample_task()
+
+    records = [
+        (MeasureInput(target, tsk, tsk.config_space.get(0)), MeasureResult((0.1,), 0, 2.3, 0)),
+        (MeasureInput(target, tsk, tsk.config_space.get(1)), MeasureResult((0.3,), 0, 2.3, 0)),
+        (MeasureInput(target, tsk, tsk.config_space.get(2)), MeasureResult((0.01,), 0, 2.3, 0)),
+        (MeasureInput(target, tsk, tsk.config_space.get(4)), MeasureResult((0.4,), 0, 2.3, 0))
+    ]
+    hist_best = ApplyHistoryBest(records)
+    x = hist_best.query(target, tsk.workload)
+    assert str(x) == str(tsk.config_space.get(2))
+
+
+if __name__ == "__main__":
+    test_load_dump()
+    test_apply_history_best()
+    test_file_io()

tests/python/unittest/test_autotvm_space.py

+"""Test space definition primitives"""
+
+import tvm
+from tvm.autotvm.task.space import ConfigSpace
+
+def gemm_func(cfg, N):
+    A = tvm.placeholder((N, N), name='A')
+    B = tvm.placeholder((N, N), name='B')
+
+    k = tvm.reduce_axis((0, N), name='k')
+    C = tvm.compute((N, N), lambda i, j: tvm.sum(A[i, k] * B[k, j], axis=[k]), name='C')
+
+    s = tvm.create_schedule([C.op])
+
+    y, x = s[C].op.axis
+
+    cfg.define_split('tile_y', cfg.axis(y), num_outputs=2)
+    cfg.define_split('tile_x', cfg.axis(x), num_outputs=2)
+
+    return s, [A, B, C]
+
+def test_split():
+    cfg = ConfigSpace()
+
+    gemm_func(cfg, 128)
+    assert len(cfg) == 64
+    assert len(cfg.space_map['tile_y']) == 8
+
+if __name__ == '__main__':
+    test_split()

tests/python/unittest/test_pass_verify_gpu_code.py

@@ -31,14 +31,14 @@ def test_shared_memory():
         with tvm.build_config(**{"add_lower_pass": [
             (2, get_verify_pass(valid,
                                 max_shared_memory_per_block=4 * M - 1,
-                                max_thread_per_block=M))]}):
+                                max_threads_per_block=M))]}):
             tvm.build(s, [A, B], target)
         assert not valid[0]
 
         with tvm.build_config(**{"add_lower_pass": [
             (2, get_verify_pass(valid,
                                 max_shared_memory_per_block=4 * M,
-                                max_thread_per_block=M))]}):
+                                max_threads_per_block=M))]}):
             tvm.build(s, [A, B], target)
         assert valid[0]
 
@@ -66,14 +66,14 @@ def test_local_memory():
         with tvm.build_config(**{"add_lower_pass": [
             (2, get_verify_pass(valid,
                                 max_local_memory_per_block=4 * M - 1,
-                                max_thread_per_block=1))]}):
+                                max_threads_per_block=1))]}):
             tvm.build(s, [A, B], target)
         assert not valid[0]
 
         with tvm.build_config(**{"add_lower_pass": [
             (2, get_verify_pass(valid,
                                 max_local_memory_per_block=4 * M,
-                                max_thread_per_block=1))]}):
+                                max_threads_per_block=1))]}):
             tvm.build(s, [A, B], target)
         assert valid[0]
 
@@ -101,21 +101,21 @@ def test_num_thread():
         with tvm.build_config(**{"add_lower_pass": [
             (2, get_verify_pass(valid,
                                 max_shared_memory_per_block=0,
-                                max_thread_per_block=N - 1))]}):
+                                max_threads_per_block=N - 1))]}):
             tvm.build(s, [A, B], target)
         assert not valid[0]
 
         with tvm.build_config(**{"add_lower_pass": [
             (2, get_verify_pass(valid,
                                 max_shared_memory_per_block=0,
-                                max_thread_per_block=N))]}):
+                                max_threads_per_block=N))]}):
             tvm.build(s, [A, B], target)
         assert valid[0]
 
         with tvm.build_config(**{"add_lower_pass": [
             (2, get_verify_pass(valid,
                                 max_shared_memory_per_block=0,
-                                max_thread_per_block=N,
+                                max_threads_per_block=N,
                                 max_thread_y=M-1))]}):
             tvm.build(s, [A, B], target)
         assert not valid[0]
@@ -123,7 +123,7 @@ def test_num_thread():
         with tvm.build_config(**{"add_lower_pass": [
             (2, get_verify_pass(valid,
                                 max_shared_memory_per_block=0,
-                                max_thread_per_block=N,
+                                max_threads_per_block=N,
                                 max_thread_y=M))]}):
             tvm.build(s, [A, B], target)
         assert valid[0]
@@ -151,14 +151,14 @@ def test_multiple_kernels():
         with tvm.build_config(**{"add_lower_pass": [
             (2, get_verify_pass(valid,
                                 max_shared_memory_per_block=0,
-                                max_thread_per_block=N - 1))]}):
+                                max_threads_per_block=N - 1))]}):
             tvm.build(s, [A, C], target)
         assert not valid[0]
 
         with tvm.build_config(**{"add_lower_pass": [
             (2, get_verify_pass(valid,
                                 max_shared_memory_per_block=0,
-                                max_thread_per_block=N))]}):
+                                max_threads_per_block=N))]}):
             tvm.build(s, [A, C], target)
         assert valid[0]
 

tests/scripts/task_python_docs.sh

@@ -8,7 +8,7 @@ make doc
 jsdoc web/tvm_runtime.js web/README.md || exit -1
 mv out docs/_build/html/jsdoc || exit -1
 
-rm -rf python/tvm/*.pyc python/tvm/*/*.pyc
+rm -rf python/tvm/*.pyc python/tvm/*/*.pyc python/tvm/*/*/*.pyc
 
 cd docs
 PYTHONPATH=`pwd`/../python make html || exit -1

tests/scripts/task_python_integration.sh

@@ -2,7 +2,7 @@
 export PYTHONPATH=python:apps/extension/python
 export LD_LIBRARY_PATH=build:${LD_LIBRARY_PATH}
 
-rm -rf python/tvm/*.pyc python/tvm/*/*.pyc
+rm -rf python/tvm/*.pyc python/tvm/*/*.pyc python/tvm/*/*/*.pyc
 
 # Test TVM
 make cython || exit -1

tests/scripts/task_python_unittest.sh

@@ -2,7 +2,7 @@
 
 export PYTHONPATH=python:topi/python
 
-rm -rf python/tvm/*.pyc python/tvm/*/*.pyc
+rm -rf python/tvm/*.pyc python/tvm/*/*.pyc python/tvm/*/*/*.pyc
 
 TVM_FFI=ctypes python -m nose -v tests/python/unittest || exit -1
 TVM_FFI=ctypes python3 -m nose -v tests/python/unittest || exit -1

tutorials/autotvm/README.txt

+Auto tuning
+-------------
+

tutorials/autotvm/tune_cuda_conv2d.py

+"""
+How to get high performance convolution kernel on NVIDIA GPU by auto-tuning
+=========================================================================
+**Author**: `Lianmin Zheng <https://https://github.com/merrymercy>`_
+
+This is an advanced tutorial for writing high performance tunable template for 
+NVIDIA GPU. By running auto-tuner on this template, we can outperform the
+vendor provided library CuDNN in many cases.
+"""
+
+import logging
+import sys
+
+import tvm
+import topi
+
+from tvm import autotvm
+
+######################################################################
+# Step 1:  Define the search space
+# ---------------------------------
+# There are plenty of useful schedule primitives in tvm. You can also find 
+# some tutorials that describe them in more details, such as 
+# (1). :doc:``Optimizing Conv2d on NVIDIA GPU <../optimize/opt_conv_cuda>`
+# (2). `Optimizing DepthwiseConv on NVIDIA GPU <https://tvm.ai/2017/08/22/Optimize-Deep-Learning-GPU-Operators-with-TVM-A-Depthwise-Convolution-Example.html>`_
+# 
+# However, their implementations are manually tuned for some special input
+# shapes. In this section, we build a large enough space to cover
+# the techniques used in these tutorials. Then we rely on the efficient auto-tuner
+# to search through this space and pick some good configurations.
+# 
+# If you are familiar with writing cuda schedule, you can find the following
+# template is very general. Actually this template can be easily modified 
+# to tune other operators such as depthwise convolution and gemm.
+# In order to fully understand this template, you should be familiar with
+# the schedule primitives and auto tuning API. You can refer to the above
+# tutorials and :doc:`autotvm tutorial <tune_simple_template>`
+#
+# It is worth noting that the search space for a conv2d operator
+# can be very large (at the level of 10^9 for some input shapes)
+#
+
+@autotvm.template
+def conv2d_no_batching(N, H, W, CI, CO, KH, KW, stride, padding):
+    assert N == 1, "Only consider batch_size = 1 in this template"
+
+    data = tvm.placeholder((N, CI, H, W), name='data')
+    kernel = tvm.placeholder((CO, CI, KH, KW), name='kernel')
+    conv = topi.nn.conv2d_nchw(data, kernel, stride, padding, 'float32')
+    s = tvm.create_schedule([conv.op])
+
+    # inline padding
+    pad_data = s[conv].op.input_tensors[0]
+    s[pad_data].compute_inline()
+    data, raw_data = pad_data, data
+
+    output = conv
+    OL = s.cache_write(conv, 'local')
+
+    # create cache stage
+    AA = s.cache_read(data, 'shared', [OL])
+    WW = s.cache_read(kernel, 'shared', [OL])
+    AL = s.cache_read(AA, 'local', [OL])
+    WL = s.cache_read(WW, 'local', [OL])
+
+    # tile and bind spatial axes
+    n, f, y, x = s[output].op.axis
+    cfg = autotvm.get_config()
+    cfg.define_split("tile_f", cfg.axis(f), num_outputs=4)
+    cfg.define_split("tile_y", cfg.axis(y), num_outputs=4)
+    cfg.define_split("tile_x", cfg.axis(x), num_outputs=4)
+    bf, vf, tf, fi = cfg["tile_f"].apply(s, output, f)
+    by, vy, ty, yi = cfg["tile_y"].apply(s, output, y)
+    bx, vx, tx, xi = cfg["tile_x"].apply(s, output, x)
+    kernel_scope = n  # this is the scope to attach global config inside this kernel
+
+    s[output].bind(bf, tvm.thread_axis("blockIdx.z"))
+    s[output].bind(by, tvm.thread_axis("blockIdx.y"))
+    s[output].bind(bx, tvm.thread_axis("blockIdx.x"))
+    s[output].bind(vf, tvm.thread_axis("vthread"))
+    s[output].bind(vy, tvm.thread_axis("vthread"))
+    s[output].bind(vx, tvm.thread_axis("vthread"))
+    s[output].bind(tf, tvm.thread_axis("threadIdx.z"))
+    s[output].bind(ty, tvm.thread_axis("threadIdx.y"))
+    s[output].bind(tx, tvm.thread_axis("threadIdx.x"))
+    s[output].reorder(n, bf, by, bx, vf, vy, vx, tf, ty, tx, fi, yi, xi)
+    s[OL].compute_at(s[output], tx)
+
+    # tile and bind reduction axes
+    n, f, y, x = s[OL].op.axis
+    rc, ry, rx = s[OL].op.reduce_axis
+    cfg.define_split("tile_rc", cfg.axis(rc), num_outputs=3)
+    cfg.define_split("tile_ry", cfg.axis(ry), num_outputs=3)
+    cfg.define_split("tile_rx", cfg.axis(rx), num_outputs=3)
+    rco, rcm, rci = cfg['tile_rc'].apply(s, OL, rc)
+    ryo, rym, ryi = cfg['tile_rx'].apply(s, OL, ry)
+    rxo, rxm, rxi = cfg['tile_ry'].apply(s, OL, rx)
+    s[OL].reorder(rco, ryo, rxo, rcm, rym, rxm, rci, ryi, rxi, n, f, y, x)
+
+    s[AA].compute_at(s[OL], rxo)
+    s[WW].compute_at(s[OL], rxo)
+    s[AL].compute_at(s[OL], rxm)
+    s[WL].compute_at(s[OL], rxm)
+
+    # cooperative fetching
+    for load in [AA, WW]:
+        n, f, y, x = s[load].op.axis
+        fused = s[load].fuse(n, f, y, x)
+        tz, fused = s[load].split(fused, nparts=cfg["tile_f"].size[2])
+        ty, fused = s[load].split(fused, nparts=cfg["tile_y"].size[2])
+        tx, fused = s[load].split(fused, nparts=cfg["tile_x"].size[2])
+        s[load].bind(tz, tvm.thread_axis("threadIdx.z"))
+        s[load].bind(ty, tvm.thread_axis("threadIdx.y"))
+        s[load].bind(tx, tvm.thread_axis("threadIdx.x"))
+
+    # tune unroll
+    cfg.define_knob("auto_unroll_max_step", [0, 512, 1500])
+    cfg.define_knob("unroll_explicit", [0, 1])
+    s[output].pragma(kernel_scope, 'auto_unroll_max_step', cfg['auto_unroll_max_step'].val)
+    s[output].pragma(kernel_scope, 'unroll_explicit', cfg['unroll_explicit'].val)
+
+    return s, [raw_data, kernel, conv]
+
+######################################################################
+# Step 2:  Search through the space
+# ---------------------------------
+# We pick the last layer on resnet as test case.
+# Since our space is very large, :code:`XGBoostTuner` is most suitable
+# for our case. Here we only do 20 trials for demonstration.
+# In practice, making 1000 trials usually can find some good kernels
+# for this template
+
+# logging config (for printing tuning log to screen)
+logging.basicConfig(level=logging.INFO, stream=sys.stdout)
+
+# the last layer in resnet 
+task = autotvm.task.create(conv2d_no_batching,
+                           args=(1, 7, 7, 512, 512, 3, 3, (1, 1), (1, 1)),
+                           target='cuda')
+print(task.config_space)
+
+# use local gpu, measure 5 times for every config to reduce variance
+# run 8 parallel threads for compilation
+measure_option = autotvm.measure_option(mode='local',
+                                        number=10,
+                                        parallel_num=8,
+                                        timeout=20)
+
+# begin tuning, log records to file `cache.tsv`
+tuner = autotvm.tuner.XGBTuner(task)
+tuner.tune(n_trial=20,
+           measure_option=measure_option,
+           callbacks=[autotvm.callback.log_to_file('cache.tsv')])
+
+# get best config from cache file
+dispatch_context = autotvm.apply_history_best("cache.tsv")
+best_config = dispatch_context.query(task.target, task.workload)
+print("\nBest config:")
+print(best_config)
+

tutorials/autotvm/tune_simple_template.py

+"""
+Writing tunable template and Using auto-tuner
+=============================================
+**Author**: `Lianmin Zheng <https://https://github.com/merrymercy>`_
+
+This is an introduction tutorial to the auto-tuning module in tvm.
+
+There are two steps in auto-tuning.
+The first step is defining a search space.
+The second step is running a search algorithm to explore through this space.
+In this tutorial, you can learn how to perform these two steps in tvm.
+The whole workflow is illustrated by a matrix multiplication example.
+"""
+
+import logging
+import sys
+
+import numpy as np
+import tvm
+
+# the module is called `autotvm`
+from tvm import autotvm
+
+######################################################################
+# Step 1:  Define the search space
+# ---------------------------------
+# In this section, we will rewrite a deterministic tvm schedule code to a
+# tunable schedule template. You can regard the process of search space definition
+# as the parametrization of our exiting schedule code.
+#
+# To begin with, here is how we implement a blocked matrix multiplication in tvm
+
+# Matmul V0: Constant tiling factor
+def matmul_v0(N, L, M, dtype):
+    A = tvm.placeholder((N, L), name='A', dtype=dtype)
+    B = tvm.placeholder((L, M), name='B', dtype=dtype)
+
+    k = tvm.reduce_axis((0, L), name='k')
+    C = tvm.compute((N, M), lambda i, j: tvm.sum(A[i, k] * B[k, j], axis=k), name='C')
+    s = tvm.create_schedule(C.op)
+
+    # schedule
+    y, x = s[C].op.axis
+    k = s[C].op.reduce_axis[0]
+
+    yo, yi = s[C].split(y, 8)
+    xo, xi = s[C].split(x, 8)
+
+    s[C].reorder(yo, xo, k, yi, xi)
+
+    return s, [A, B, C]
+
+#####################################################################
+# Parametrize the schedule
+# ^^^^^^^^^^^^^^^^^^^^^^^^^
+# In the previous schedule code, we use a constant "8" as tiling factor.
+# However, it might not be the best one because the best tiling factor depends
+# on real hardware environment and input shape.
+#
+# If you want the schedule code to be portable across a wider range of input shapes
+# and target hardware, it is better to define a set of candidate values and
+# pick the best one according to the measurement results on target hardware.
+#
+# In autotvm, we can define a tunable parameter, or a "knob" for such kind of value.
+
+# Matmul V1: List candidate values
+@autotvm.template  # 1. use a decorator
+def matmul_v1(N, L, M, dtype):
+    A = tvm.placeholder((N, L), name='A', dtype=dtype)
+    B = tvm.placeholder((L, M), name='B', dtype=dtype)
+
+    k = tvm.reduce_axis((0, L), name='k')
+    C = tvm.compute((N, M), lambda i, j: tvm.sum(A[i, k] * B[k, j], axis=k), name='C')
+    s = tvm.create_schedule(C.op)
+
+    # schedule
+    y, x = s[C].op.axis
+    k = s[C].op.reduce_axis[0]
+
+    # 2. get the config object
+    cfg = autotvm.get_config()
+
+    # 3. define search space
+    cfg.define_knob("tile_y", [1, 2, 4, 8, 16])
+    cfg.define_knob("tile_x", [1, 2, 4, 8, 16])
+
+    # 4. schedule according to config
+    yo, yi = s[C].split(y, cfg['tile_y'].val)
+    xo, xi = s[C].split(x, cfg['tile_x'].val)
+
+    s[C].reorder(yo, xo, k, yi, xi)
+
+    return s, [A, B, C]
+
+###############################################################################
+# Here we make four modifications to the previous schedule code and get
+# a tunable "template". We can explain the modifications one by one.
+#
+# 1. Use a decorator to mark this function as a simple template
+# 2. Get a config object:
+#    You can regard this :code:`cfg` as an argument of this function but
+#    we obtain it in a different way. With this argument, this function is no longer
+#    a deterministic schedule code. Instead, we can pass different configurations to
+#    this function and get different schedules, so this function is a "template".
+#
+#    To make the template function more compact, we do two things in a single function.
+#    (1) define a search space and (2) schedule according to an entity in this space.
+#    To achieve this, we make :code:`cfg` be either
+#    a :any:`ConfigSpace` or a :any:`ConfigEntity` object.
+#
+#    When it is a :any:`ConfigSpace`, it will collect all tunable knobs in this function and
+#    build the search space.
+#    When it is a :any:`ConfigEntity`, it will ignore all space definition API
+#    (namely, :code:`cfg.define_XXXXX(...)`).   Instead, it stores deterministic values for
+#    all tunable knobs, and we schedule according to these values.
+#
+#    During auto-tuning, we will first call this template with a :any:`ConfigSpace`
+#    object to build the search space. Then we call this template with different :any:`ConfigEntity`
+#    in the built space to get different schedules. Finally we will measure the code generated by
+#    different schedules and pick the best one.
+#
+# 3. Define two tunable knobs. The first one is :code:`tile_y` with
+#    5 possible values. The second one is :code:`tile_x` with a same
+#    list of possible values. These two knobs are independent, so they
+#    span a search space with size = 5x5 = 25
+# 4. Schedule according to the deterministic values in :code:`cfg`
+#
+
+#####################################################################
+# Use better space definition API
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+# In the previous template, we manually list all possible values for a knob.
+# This is the lowest level API to define the space.
+# However, we also provide another set of API to make the space definition
+# easier and smarter. It is recommended to use this set of high level API.
+#
+# In the flowing example, we use :any:`ConfigSpace.define_split` to define a split
+# knob. It will enumerate all the possible ways to split an axis and construct
+# the space.
+#
+# We also have :any:`ConfigSpace.define_reorder` for reorder knob and
+# :any:`ConfigSpace.define_annotate` for annotation like unroll, vectorization,
+# thread binding.
+# When the high level API cannot meet your requirement, you can always fall
+# back to use low level API.
+
+@autotvm.template
+def matmul(N, L, M, dtype):
+    A = tvm.placeholder((N, L), name='A', dtype=dtype)
+    B = tvm.placeholder((L, M), name='B', dtype=dtype)
+
+    k = tvm.reduce_axis((0, L), name='k')
+    C = tvm.compute((N, M), lambda i, j: tvm.sum(A[i, k] * B[k, j], axis=k), name='C')
+    s = tvm.create_schedule(C.op)
+
+    # schedule
+    y, x = s[C].op.axis
+    k = s[C].op.reduce_axis[0]
+
+    ##### define space begin #####
+    cfg = autotvm.get_config()
+    cfg.define_split("tile_y", y, num_outputs=2)
+    cfg.define_split("tile_x", x, num_outputs=2)
+    ##### define space end #####
+
+    # schedule according to config
+    yo, yi = cfg["tile_y"].apply(s, C, y)
+    xo, xi = cfg["tile_x"].apply(s, C, x)
+
+    s[C].reorder(yo, xo, k, yi, xi)
+
+    return s, [A, B, C]
+
+######################################################################
+# .. note:: More Explanation on :code:`cfg.defile_split`
+#
+#  In this template, :code:`cfg.define_split("tile_y", y, num_outputs=2)` will enumerate
+#  all possible combinations that can split axis y into two axes with factors of the length of y.
+#  For example, if the length of y is 32 and we want to split it into two axes
+#  using factors of 32, then there are 6 possible values for
+#  (length of outer axis, length of inner axis) pair, namely
+#  (32, 1), (16, 2), (8, 4), (4, 8), (2, 16) or (1, 32).
+#  They are just the 6 possible values of `tile_y`.
+#
+#  During schedule, :code:`cfg["tile_y"]` is a :code:`SplitEntity` object.
+#  We stores the lengths of outer axes and inner axes in :code:`cfg['tile_y'].size`
+#  (a tuple with two elements).
+#  In this template, we apply it by using :code:`yo, yi = cfg['tile_y'].apply(s, C, y)`.
+#  Actually, this is equivalent to
+#  :code:`yo, yi = s[C].split(y, cfg["tile_y"].size[1])`
+#  or  :code:`yo, yi = s[C].split(y, nparts=cfg['tile_y"].size[0])`
+#
+#  The advantage of using cfg.apply API is that it makes multi-level split
+#  (when num_outputs >= 3) easier.
+
+######################################################################
+# Step 2:  Search through the space
+# ---------------------------------
+# In step 1, we build the search space by extending our old schedule code
+# into a template. The next step is to pick a tuner and explore in this space.
+#
+# Auto-tuners in tvm
+# ^^^^^^^^^^^^^^^^^^
+# The job for a tuner can be described by following pseudo code
+#
+#   .. code-block:: c
+#
+#    ct = 0
+#    while ct < max_number_of_trials:
+#        propose a batch of configs
+#        measure this batch of configs on real hardware and get results
+#        ct += batch_size
+#
+# When proposing the next batch of configs, the tuner can take different strategies. We
+# provide four tuners with different strategies in autotvm.
+#
+# * :any:`RandomTuner`: Enumerate the space in a random order
+# * :any:`GridSearchTuner`: Enumerate the space in a grid search order
+# * :any:`GATuner`: Using genetic algorithm to search through the space
+# * :any:`XGBTuner`: Uses a model based method. Train a XGBoost model to predict the speed of lowered IR and pick the next batch according to the prediction.
+#
+# You can choose the tuner according to the size of your space, your time budget and other factors.
+# For example, if your space is very small (less than 1000), a gridsearch tuner or a
+# random tuner is good enough. If your space is at the level of 10^9 (this is the space
+# size of a conv2d operator on CUDA GPU), XGBoostTuner can explore more efficiently
+# and find better configs.
+
+################################################################
+# Begin tuning
+# ^^^^^^^^^^^^
+# Here we continue our matrix multiplication example.
+# First we should create a tuning task.
+# We can also inspect the initialized search space.
+# In this case, for a 512x512 square matrix multiplication, the space size
+# is 10x10=100
+N, L, M = 512, 512, 512
+task = autotvm.task.create(matmul, args=(N, L, M, 'float32'), target='llvm')
+print(task.config_space)
+
+################################################################
+# Then we need to define how to measure the generated code and pick a tuner.
+# Since our space is small, a random tuner is just okay.
+# 
+# We only make 10 trials in this tutorial for demonstration. In practice,
+# you can do more trials according to your time budget.
+# We will log the tuning results into a cache file. This file can be 
+# used to get the best config later.
+
+# logging config (for printing tuning log to screen)
+logging.basicConfig(level=logging.INFO, stream=sys.stdout)
+
+# use local cpu, measure 5 times for every config to reduce variance
+measure_option = autotvm.measure_option(mode='local',
+                                        number=5)
+
+# begin tuning, log records to file `cache.tsv`
+tuner = autotvm.tuner.RandomTuner(task)
+tuner.tune(n_trial=10,
+           measure_option=measure_option,
+           callbacks=[autotvm.callback.log_to_file('cache.tsv')])
+
+#########################################################################
+# Finally we apply history best from the cache file and check its correctness.
+# We can call the function :code:`matmul` directly under the 
+# :any:`autotvm.apply_history_best` context. When we call this function,
+# it will query the dispatch context with its argument and get the best config 
+# with the same argument.
+
+# apply history best from log file
+with autotvm.apply_history_best('cache.tsv'):
+    with tvm.target.create("llvm"):
+        s, arg_bufs = matmul(N, L, M, 'float32')
+        func = tvm.build(s, arg_bufs)
+
+# check correctness
+a_np = np.random.uniform(size=(N, L)).astype(np.float32)
+b_np = np.random.uniform(size=(L, M)).astype(np.float32)
+c_np = a_np.dot(b_np)
+
+c_tvm = tvm.nd.empty(c_np.shape)
+func(tvm.nd.array(a_np), tvm.nd.array(b_np), c_tvm)
+
+np.testing.assert_allclose(c_np, c_tvm.asnumpy(), rtol=1e-2)
+