tvm.autotvm#
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
- tvm.autotvm.apply_history_best(records)#
Apply the history best config
Parameters#
- recordsNone, Records, or iterator of Records objects, where a
Records object is a path-like object, a file-like object, or an iterator of (MeasureInput, MeasureResult).
Collection of tuning records. If multiple Records objects are passed, their contents will be merged.
- 参数:
records (None | str | bytes | Path | TextIOBase | Iterable[Tuple[MeasureInput, MeasureResult]] | Iterable[str | bytes | Path | TextIOBase | Iterable[Tuple[MeasureInput, MeasureResult]]])
tvm.autotvm.measure#
User facing API for specifying how to measure the generated code
- class tvm.autotvm.measure.MeasureInput(target, task, config)[源代码]#
Stores all the necessary inputs for a measurement.
Parameters#
- targettvm.target.Target
The target device
- tasktask.Task
Task function
- configConfigEntity
Specific configuration.
- class tvm.autotvm.measure.MeasureResult(costs, error_no, all_cost, timestamp)[源代码]#
Stores all the results of a measurement
Parameters#
- costs: Array of float or Array of Exception
If no error occurs during measurement, it is an array of measured running times. If an error occurs during 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.
- tvm.autotvm.measure.measure_option(builder, runner)[源代码]#
Set options for measure. To measure a config, we will build it and run it. So we have to set options for these two steps. They have their own options on timeout, parallel, etc.
Parameters#
- builder: Builder
Specify how to build programs
- runner: Runner
Specify how to run programs
Examples#
# example setting for using local devices >>> measure_option = autotvm.measure_option( >>> builder=autotvm.LocalBuilder(), # use all local cpu cores for compilation >>> runner=autotvm.LocalRunner( # measure them sequentially >>> number=10, >>> timeout=5) >>> )
# example setting for using remote devices >>> measure_option = autotvm.measure_option( >>> builder=autotvm.LocalBuilder(), # use all local cpu cores for compilation >>> runner=autotvm.RPCRunner( >>> 'rasp3b', 'locahost', 9190, # device key, host and port of the rpc tracker >>> number=4, >>> timeout=4) # timeout of a run on the device. RPC request waiting time is excluded. >>>)
Note#
To make measurement results accurate, you should pick the correct value for the argument number and repeat in Runner(). Some devices need a certain minimum running time to "warm up," such as GPUs that need time to reach a performance power state. Using min_repeat_ms can dynamically adjusts number, so it is recommended. The typical value for NVIDIA GPU is 150 ms.
- tvm.autotvm.measure.create_measure_batch(task, option)[源代码]#
Get a standard measure_batch function.
Parameters#
- task: tvm.autotvm.task.Task
The tuning task
- option: dict
The option for measuring generated code. You should use the return value of function
measure_optionfor this argument.
Returns#
- measure_batch: callable
a callback function to measure a batch of configs
- class tvm.autotvm.measure.measure_methods.LocalBuilder(timeout=10, n_parallel=None, build_kwargs=None, build_func='default', do_fork=False, runtime=None)[源代码]#
Run compilation on local machine
Parameters#
- timeout: float
The timeout of a compilation
- n_parallel: int
The number of tasks run in parallel. "None" will use all cpu cores
- build_kwargs: dict
If supplied, additional kwargs passed to build_func. Overrides any build_kwargs supplied by the Runner.
- build_func: callable or str
If is 'default', use default build function If is 'ndk', use function for android ndk If id 'stackvm', use function for stackvm If is callable, use it as custom build function, expect lib_format field.
- do_fork: bool
If False, do not fork when building. Requires n_parallel=1.
- runtime: Optional[Runtime]
Specify the runtime to generate artifacts for
- class tvm.autotvm.measure.measure_methods.RPCRunner(key, host, port, priority=1, timeout=10, n_parallel=None, number=4, repeat=3, min_repeat_ms=0, cooldown_interval=0.1, enable_cpu_cache_flush=False, module_loader=None)[源代码]#
Run generated code on remove devices. This function will ask a RPC Tracker to get device for measurement.
Parameters#
- timeout: float
The timeout of a RPCRunner measurement task
- n_parallel: int
The number of tasks run in parallel. "None" will use all cpu cores
- key: str
The key of the device registered in the tracker
- host: str
The host address of RPC Tracker
- port: int
The port of RPC Tracker
- number: int
The number of times to run the generated code for taking average. We call these runs as one repeat of measurement.
- repeatint, optional
The number of times to repeat the measurement. In total, the generated code will be run (1 + number x repeat) times, where the first "1" is warm up and will be discarded. The returned result contains repeat costs, each of which is an average of number costs.
- min_repeat_ms: int, optional
The minimum duration of one repeat in milliseconds. By default, one repeat contains number runs. If this parameter is set, the parameters number will be dynamically adjusted to meet the minimum duration requirement of one repeat. i.e., When the run time of one repeat falls below this time, the number parameter will be automatically increased.
- cooldown_interval: float, optional
The cool down interval between two measurements.
- enable_cpu_cache_flush: bool
Whether to flush cache on CPU between repeated measurements. Flushing cache can make the measured latency of one operator closer to its actual latency during end-to-end inference. To make this option effective, the argument number should also be set to 1. This is only has effect on CPU task.
- module_loaderModuleLoader
If given, a context manager that loads the module to be timed into the remote runtime. If not given, default_module_loader is used.
- class tvm.autotvm.measure.measure_methods.LocalRunner(timeout=10, number=4, repeat=3, min_repeat_ms=0, cooldown_interval=0.1, enable_cpu_cache_flush=False, module_loader=None)[源代码]#
Run generated code on local devices.
Parameters#
- timeout: float
The timeout of a compilation
- number: int
The number of times to run the generated code for taking average. We call these runs as one repeat of measurement.
- repeatint, optional
The 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 and will be discarded. The returned result contains repeat costs, each of which is an average of number costs.
- min_repeat_ms: int, optional
The minimum duration of one repeat in milliseconds. By default, one repeat contains number runs. If this parameter is set, the parameters number will be dynamically adjusted to meet the minimum duration requirement of one repeat. i.e., When the run time of one repeat falls below this time, the number parameter will be automatically increased.
- cooldown_interval: float, optional
The cool down interval between two measurements.
- enable_cpu_cache_flush: bool
Whether to flush cache on CPU between repeated measurements. Flushing cache can make the measured latency of one operator closer to its actual latency during end-to-end inference. To make this option effective, the argument number should also be set to 1. This is only has effect on CPU task.
Note#
This is a "fake" local mode. We start a silent rpc tracker and rpc server for the user. In this way we reuse timeout/isolation mechanism in RPC infrastructure.
tvm.autotvm.tuner#
A tuner takes a task as input. It proposes some promising ConfigEntity
in the ConfigSpace and measure them on the real hardware. Then it
proposed the next batch of ConfigEntity according to the measure results.
This tuning loop is repeated.
- class tvm.autotvm.tuner.Tuner(task, **kwargs)[源代码]#
Base class for tuners
Parameters#
- task: autotvm.task.Task
Tuning Task
- load_history(data_set, min_seed_records=500)[源代码]#
load history data for transfer learning
Parameters#
- data_set: Array of (autotvm.measure.MeasureInput, autotvm.measure.MeasureResult) pair
Previous tuning records
- min_seed_records: int
Defaults to 500. Indicates the minimum number of records to train the tuner with. If there are less than min_seed_records number of records in data_set, no training of the tuner will be done.
- next_batch(batch_size)[源代码]#
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
- set_error_threshold(threshold)[源代码]#
Modify error counter threshold, which controls switch to debug mode
Parameters#
threshold: New threshold value
- tune(n_trial, measure_option, early_stopping=None, callbacks=(), si_prefix='G')[源代码]#
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.
- early_stopping: int, optional
Early stop the tuning when not finding better configs in this number of trials
- 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.
- si_prefix: str
One of tvm.autotvm.utils.SI_PREFIXES. The SI prefix to use when reporting FLOPS.
- class tvm.autotvm.tuner.RandomTuner(task, range_idx=None)[源代码]#
Enumerate the search space in a random order
Parameters#
- task: autotvm.task.Task
Tuning Task
- range_idx: Optional[Tuple[int, int]]
A tuple of index range to random
- load_history(data_set, min_seed_records=500)#
load history data for transfer learning
Parameters#
- data_set: Array of (autotvm.measure.MeasureInput, autotvm.measure.MeasureResult) pair
Previous tuning records
- min_seed_records: int
Defaults to 500. Indicates the minimum number of records to train the tuner with. If there are less than min_seed_records number of records in data_set, no training of the tuner will be done.
- next_batch(batch_size)[源代码]#
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
- reset()#
reset the status of tuner
- set_error_threshold(threshold)#
Modify error counter threshold, which controls switch to debug mode
Parameters#
threshold: New threshold value
- tune(n_trial, measure_option, early_stopping=None, callbacks=(), si_prefix='G')#
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.
- early_stopping: int, optional
Early stop the tuning when not finding better configs in this number of trials
- 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.
- si_prefix: str
One of tvm.autotvm.utils.SI_PREFIXES. The SI prefix to use when reporting FLOPS.
- class tvm.autotvm.tuner.GridSearchTuner(task, range_idx=None)[源代码]#
Enumerate the search space in a grid search order
- load_history(data_set, min_seed_records=500)#
load history data for transfer learning
Parameters#
- data_set: Array of (autotvm.measure.MeasureInput, autotvm.measure.MeasureResult) pair
Previous tuning records
- min_seed_records: int
Defaults to 500. Indicates the minimum number of records to train the tuner with. If there are less than min_seed_records number of records in data_set, no training of the tuner will be done.
- next_batch(batch_size)[源代码]#
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
- reset()#
reset the status of tuner
- set_error_threshold(threshold)#
Modify error counter threshold, which controls switch to debug mode
Parameters#
threshold: New threshold value
- tune(n_trial, measure_option, early_stopping=None, callbacks=(), si_prefix='G')#
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.
- early_stopping: int, optional
Early stop the tuning when not finding better configs in this number of trials
- 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.
- si_prefix: str
One of tvm.autotvm.utils.SI_PREFIXES. The SI prefix to use when reporting FLOPS.
- class tvm.autotvm.tuner.GATuner(task, pop_size=100, elite_num=3, mutation_prob=0.1)[源代码]#
Tuner with genetic algorithm. This tuner does not have a cost model so it always run measurement on real machines. This tuner expands the
ConfigEntityas 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
- load_history(data_set, min_seed_records=500)[源代码]#
load history data for transfer learning
Parameters#
- data_set: Array of (autotvm.measure.MeasureInput, autotvm.measure.MeasureResult) pair
Previous tuning records
- min_seed_records: int
Defaults to 500. Indicates the minimum number of records to train the tuner with. If there are less than min_seed_records number of records in data_set, no training of the tuner will be done.
- next_batch(batch_size)[源代码]#
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
- reset()#
reset the status of tuner
- set_error_threshold(threshold)#
Modify error counter threshold, which controls switch to debug mode
Parameters#
threshold: New threshold value
- tune(n_trial, measure_option, early_stopping=None, callbacks=(), si_prefix='G')#
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.
- early_stopping: int, optional
Early stop the tuning when not finding better configs in this number of trials
- 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.
- si_prefix: str
One of tvm.autotvm.utils.SI_PREFIXES. The SI prefix to use when reporting FLOPS.
- class tvm.autotvm.tuner.XGBTuner(task, plan_size=64, feature_type='itervar', loss_type='reg', num_threads=None, optimizer='sa', diversity_filter_ratio=None, log_interval=50)[源代码]#
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' are good. 'itervar' is more accurate but 'knob' is much faster. There are some constraints on 'itervar', if you meet problems with feature extraction when using 'itervar', you can switch to 'knob'.
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. If is 'rank-binary', use pairwise rank loss with binarized labels 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.
- log_interval: int = 50
The verbose level. If is 0, output nothing. Otherwise, output debug information every verbose iterations.
- load_history(data_set, min_seed_records=500)#
load history data for transfer learning
Parameters#
- data_set: Array of (autotvm.measure.MeasureInput, autotvm.measure.MeasureResult) pair
Previous tuning records
- min_seed_records: int
Defaults to 500. Indicates the minimum number of records to train the tuner with. If there are less than min_seed_records number of records in data_set, no training of the tuner will be done.
- next_batch(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
- reset()#
reset the status of tuner
- set_error_threshold(threshold)#
Modify error counter threshold, which controls switch to debug mode
Parameters#
threshold: New threshold value
- tune(*args, **kwargs)[源代码]#
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.
- early_stopping: int, optional
Early stop the tuning when not finding better configs in this number of trials
- 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.
- si_prefix: str
One of tvm.autotvm.utils.SI_PREFIXES. The SI prefix to use when reporting FLOPS.
Namespace of callback utilities of AutoTVM
- tvm.autotvm.tuner.callback.log_to_database(db)[源代码]#
Save the tuning records to a database object.
Parameters#
- db: Database
The database
- tvm.autotvm.tuner.callback.log_to_file(file_out, protocol='json')[源代码]#
Log the tuning records into file. The rows of the log are stored in the format of autotvm.record.encode.
Parameters#
- file_outFile or str
The file to log to.
- protocol: str, optional
The log protocol. Can be 'json' or 'pickle'
Returns#
- callbackcallable
Callback function to do the logging.
tvm.autotvm.task#
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.
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.
- exception tvm.autotvm.task.task.FlopCalculationError[源代码]#
Error happens when estimating FLOP for a compute op
- class tvm.autotvm.task.task.MissingTask(taskname)[源代码]#
Dummy task template for a task lookup which cannot be resolved. This can occur if the task being requested from _lookup_task() has not been imported in this run.
- 参数:
taskname (str)
- class tvm.autotvm.task.task.Task(name, args)[源代码]#
A Tunable Task
Parameters#
- name: str
The name of the task.
- args: Tuple
Positional argument of func
- instantiate(config)[源代码]#
Instantiate this task function (template) with a config. Returns corresponding schedule.
Parameters#
- config: template.ConfigEntity
parameter config for this template
Returns#
- sch: tvm.te.schedule.Schedule
The tvm schedule
- arg_bufs: Array of te.tensor.Tensor
The input/output buffers
- class tvm.autotvm.task.task.TaskTemplate[源代码]#
Task template is used to creates a tunable AutoTVM task.
It can be defined by a pair of compute and schedule function using _register_task_compute and _register_task_schedule, or by a customized task creation function that is more flexible using _register_customized_task.
Note that when customized func is registered, compute and schedule function will be ignored
- tvm.autotvm.task.task._register_customized_task(name, func=None)[源代码]#
Register a customized function to AutoTVM task.
Parameters#
- name: str
The task name
- func: None or callable
If it is None, return a decorator. If is callable, decorate this function.
Returns#
- decorator: callable
A decorator
- tvm.autotvm.task.task._register_task_compute(name, func=None)[源代码]#
Register compute function to autotvm task
Parameters#
- name: str
The task name
- func: None or callable
If it is None, return a decorator. If is callable, decorate this function.
Returns#
- decorator: callable
A decorator
- tvm.autotvm.task.task._register_task_schedule(name, func=None)[源代码]#
Register schedule function to autotvm task
Parameters#
- name: str
The task name
- func: None or callable
If it is None, return a decorator. If is callable, decorate this function.
Returns#
- decorator: callable
A decorator
- tvm.autotvm.task.task.args_to_workload(args, task_name=None)[源代码]#
Convert argument list to hashable workload tuple. This function will convert list to tuple, tvm node to python value and flatten te.tensor.Tensor to a tuple
Parameters#
- task_namestr
The AutoTVM task name
- argslist of args
The arguments to the function
Returns#
- ret: hashable
The hashable value
- tvm.autotvm.task.task.compute_flop(sch)[源代码]#
Calculate number of FLOP (floating number operations) of the compute ops in a schedule
Parameters#
- sch: tvm.te.schedule.Schedule
schedule
Returns#
- flop: int
number of FLOP in this schedule
- tvm.autotvm.task.task.create(task_name, args, target, target_host=None)[源代码]#
Create a tuning task and initialize its search space
Parameters#
- task_namestr
The AutoTVM task name
- argsList
Positional arguments
- targetTarget
The compilation target
- target_host: Target, optional
The compilation target for host side
Returns#
- tsk: Task
a task object
- tvm.autotvm.task.task.deserialize_args(args)[源代码]#
The inverse function of
serialize_args.Parameters#
args: list of hashable or Tensor
- tvm.autotvm.task.task.get_config()[源代码]#
Get current config object
Returns#
- cfg: ConfigSpace or ConfigEntity
The current config
- tvm.autotvm.task.task.serialize_args(args)[源代码]#
serialize arguments of a topi function to a hashable tuple.
Parameters#
args: list of hashable or Tensor
- tvm.autotvm.task.task.template(task_name, func=None)[源代码]#
Decorate a function as a tunable schedule template.
Parameters#
- task_name: str
The task name
- func: None or callable
A callable template function. If it is None, return a decorator. If is callable, decorate this function.
Returns#
- func: callable
The decorated function
Examples#
The following code is a tunable template for a blocked matrix multiplication
@autotvm.template("matmul") def matmul(N, L, M, dtype): A = te.placeholder((N, L), name='A', dtype=dtype) B = te.placeholder((L, M), name='B', dtype=dtype) k = te.reduce_axis((0, L), name='k') C = te.compute((N, M), lambda i, j: te.sum(A[i, k] * B[k, j], axis=k), name='C') s = te.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]
Template configuration space.
Each template function can be parameterized 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.
- exception tvm.autotvm.task.space.InstantiationError[源代码]#
Actively detected error in instantiating a template with a config, raised by cfg.raise_error e.g. too many unrolling, too many threads in a block
- class tvm.autotvm.task.space.AnnotateEntity(anns)[源代码]#
An annotation operation with detailed parameters that can apply to axes
Parameters#
- anns: Array of string
The annotations of axes
- apply(sch, op, axes, axis_lens=None, max_unroll=None, vec_size=None, cfg=None, source=None)[源代码]#
Apply annotation to an array of axes
Parameters#
- sch: tvm.te.schedule.Schedule
The tvm schedule
- op: tvm.te.Operation
The stage to be applied
- axes: Array of tvm.te.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#
- axeslist of tvm.te.schedule.IterVar
The transformed axes
- class tvm.autotvm.task.space.AnnotateSpace(axes, policy, **kwargs)[源代码]#
The parameter space for annotating an array of axes
- class tvm.autotvm.task.space.Axis(space, index)#
- index#
Alias for field number 1
- space#
Alias for field number 0
- class tvm.autotvm.task.space.ConfigEntity(index, code_hash, entity_map, constraints)[源代码]#
A configuration with detailed parameters
Parameters#
- index: int
index of this config in space
- code_hash: str
hash of schedule code
- entity_map: dict
map name to transform entity
- constraintslist
List of constraints
- static from_json_dict(json_dict)[源代码]#
Build a ConfigEntity from json serializable dictionary
Parameters#
- json_dict: dict
Json serializable dictionary. This should be the return value of
to_json_dict.
Returns#
- config: ConfigEntity
The corresponding config object
- get_flatten_feature()[源代码]#
flatten entities to a numerical one-dimensional feature vector
Returns#
- fea: np.array
one dimensional float32 array
- class tvm.autotvm.task.space.ConfigSpace[源代码]#
The configuration space of a schedule. Pass it as config in template to collect transformation space and build transform graph of axes
- __getitem__(name)[源代码]#
- 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
- _add_new_transform(space_class, name, axes, policy, **kwargs)[源代码]#
Add a new transform space in template
- add_flop(flop)[源代码]#
Add float operation statistics for this tuning task
Parameters#
- flop: int or float or IntImm or FloatImm
number of float operations
- static axis(var)[源代码]#
get a virtual axis (axis placeholder)
Parameters#
- var: int or tvm.te.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.
- define_annotate(name, axes, policy, **kwargs)[源代码]#
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.te.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
- define_knob(name, candidate)[源代码]#
Define a tunable knob with a list of candidates
Parameters#
- name: str
name key of that option
- candidate: list
list of candidates
- define_reorder(name, axes, policy, **kwargs)[源代码]#
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.te.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
- define_split(name, axis, policy='factors', **kwargs)[源代码]#
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.te.schedule.IterVar
axis to split
- policy: str
name of policy. If is 'factors', the tuner will try all divisible factors. If is 'power2', the tuner will try power-of-two factors less or equal to the length. If is 'verbose', the tuner will try all candidates in above two policies. If is 'candidate', try given candidates.
- **kwargs:
extra arguments for policy
max_factor:the maximum split factor (int).
filter:see examples below for how to use filter (Callable[[int], bool]).
num_outputs:the total number of axis after split (int).
no_tail:should we only include divisible numbers as split factors (bool).
candidate:(policy=candidate) manual candidate list (List).
Examples#
>>> # use custom candidates >>> cfg.define_split('tile_x', x, policy='candidate', num_outputs=3, >>> candidate=[[1, 4, 4], [4, 1, 4]])
>>> # use a filter that only accepts the split scheme whose inner most tile is less then 4 >>> cfg.define_split('tile_y', y, policy='factors', num_outputs=3, >>> filter=lambda x: x.size[-1] <= 4)
- get(index)[源代码]#
Get a config entity with detailed parameters from this space
Parameters#
- index: int
index in the space
Returns#
- config: ConfigEntity
config corresponds to the index
- get_next_index(index, n=1, start=None, end=None)[源代码]#
Returns the nth valid next index or None if out of range
Parameters#
- index: int
specifying at which position to start, inclusive
- n: int, optional
step by using to find the next index, for the opposite direction a negative number should be used
- start: list, optional
start of subrange, inclusive
- end: list, optional
end of subrange, exclusive
Returns#
- next: int
next index in the space
- get_rand_index(start=None, end=None, to_exclude=None)[源代码]#
Returns a random valid index unlisted to exclusion
Parameters#
- start: int, optional
specifying at which position to start, inclusive
- end: int, optional
specifying at which position to end, exclusive
- to_exclude: list, optional
determines unsuitable values
Returns#
- rand: int
random index in the space
备注
Excluding all valid space indexes will lead to an infinite loop.
- is_index_valid(index)[源代码]#
Checks if the index satisfies the multi_filter condition
Parameters#
- index: int
index from the range of the space
Returns#
- valid: bool
whether the index meets all the constraints
- knob2point(knob)[源代码]#
Convert knob form (vector) to point form (single integer)
Parameters#
- knob: list
knob to convert
Returns#
- point: int
point of the knob representation
- multi_filter(filter)[源代码]#
The filter can restrict combination of parameters in difference to the knob filter, that restricts only single parameter
Parameters#
- filter: function
predicate with one argument (Callable[[int], bool])
备注
Using this filter causes additional restrictions on the use of __len__. Normally, it define the count of valid indexes and the range of space, but when multi_filter enabled, it requires to use __len__ for getting the count of valid indexes or range_length for the range of space. It is recommended to use:
is_index_valid,get_next_index,get_rand_indexto bypass the spaceExamples#
>>> # Pre-requisites >>> candidates = [[16, 64], [32, 32], [64, 16]] >>> filter = lambda v: v.size[0] != 16 >>> multi_filter = lambda e: (e["tile_x"].size[0] + e["tile_y"].size[0]) <= 64
>>> # Case 1 - without filtering >>> cfg.define_split("tile_x", x, num_outputs=2, policy="candidate", candidate=candidates) >>> cfg.define_split("tile_y", y, num_outputs=2, policy="candidate", candidate=candidates) >>> # [('tile_x', [16, 64]), ('tile_y', [16, 64])],None,0 >>> # [('tile_x', [32, 32]), ('tile_y', [16, 64])],None,1 >>> # [('tile_x', [64, 16]), ('tile_y', [16, 64])],None,2 >>> # [('tile_x', [16, 64]), ('tile_y', [32, 32])],None,3 >>> # [('tile_x', [32, 32]), ('tile_y', [32, 32])],None,4 >>> # [('tile_x', [64, 16]), ('tile_y', [32, 32])],None,5 >>> # [('tile_x', [16, 64]), ('tile_y', [64, 16])],None,6 >>> # [('tile_x', [32, 32]), ('tile_y', [64, 16])],None,7 >>> # [('tile_x', [64, 16]), ('tile_y', [64, 16])],None,8
>>> # Case 2 - with filter >>> cfg.define_split("tile_x", x, num_outputs=2, policy="candidate", candidate=candidates, >>> filter=filter) >>> cfg.define_split("tile_y", y, num_outputs=2, policy="candidate", candidate=candidates, >>> filter=filter) >>> # [('tile_x', [32, 32]), ('tile_y', [32, 32])],None,0 >>> # [('tile_x', [64, 16]), ('tile_y', [32, 32])],None,1 >>> # [('tile_x', [32, 32]), ('tile_y', [64, 16])],None,2 >>> # [('tile_x', [64, 16]), ('tile_y', [64, 16])],None,3
>>> # Case 3 - with filter and multi_filter >>> cfg.define_split("tile_x", x, num_outputs=2, policy="candidate", candidate=candidates, >>> filter=filter) >>> cfg.define_split("tile_y", y, num_outputs=2, policy="candidate", candidate=candidates, >>> filter=filter) >>> cfg.multi_filter(filter=multi_filter) >>> # [('tile_x', [32, 32]), ('tile_y', [32, 32])],None,0
- point2knob(point)[源代码]#
Convert point form (single integer) to knob (vector)
Parameters#
- point: int
point to convert
Returns#
- knob: list
knob representation of the point
- raise_error(msg)[源代码]#
register error in config Using this to actively detect error when scheduling. Otherwise these error will occur during runtime, which will cost more time.
Parameters#
msg: str
- random_walk(point)[源代码]#
random walk as local transition
Parameters#
- point: int
index of the ConfigEntity
Returns#
- new_point: int
new neighborhood index
- static reduce_axis(var)#
get a virtual axis (axis placeholder)
Parameters#
- var: int or tvm.te.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.
- sample_ints(m)[源代码]#
Sample m different integer numbers from [0, self.range_length) without replacement This function is an alternative of np.random.choice when self.range_length > 2 ^ 32, in which case numpy does not work.
Parameters#
- m: int
The number of sampled int
Returns#
ints: an numpy array of size m
- subrange_length(start, end)[源代码]#
Returns the number of valid indexes within the limited range from [start, end]
Parameters#
- start: int
start of subrange, inclusive
- end: int
end of subrange, exclusive
Returns#
- count: int
number of valid indexes
- valid()[源代码]#
Check whether the config meets all the constraints
备注
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
- property dims#
Dimensions in the space
- property range_length#
Length of the index range in the space
- class tvm.autotvm.task.space.FallbackConfigEntity[源代码]#
The config entity created to support fallback
- __setitem__(name, entity)[源代码]#
set the entity(knob) of by name
Parameters#
- name: str
name of the entity
- entity: SplitEntity, ReorderEntity, AnnotateEntity, OtherOptionEntity
value of the entity
- fallback_split(name, constraints)[源代码]#
Fallback a split knob
Parameters#
- name: str
name of the knob
- constraints: List of int
The maximum tile size for every dimension. Value -1 means no constraint.
Examples#
If you use cfg.define_split('tile_0', 128, num_outputs=3), Then cfg.fallback_split('tile_0', [-1, 8, 4]) will give you cfg['tile_0'].size = [4, 8, 4]
If you use cfg.define_split('tile_0', 49, num_outputs=3), Then cfg.fallback_split('tile_0', [-1, 8, 4]) will give you cfg['tile_0'].size = [7, 7, 1]
- fallback_with_reference_log(ref_log)[源代码]#
A data driven fallback mechanism. We use tuned parameters from TopHub as reference data. For an unseen shape, we find the most similar tuned one from TopHub and mimic its parameters. Note that we are not matching by workload (e.g., input size, kernel size), but instead matching by configuration space. The idea is that if two workloads have similar configuration space, their optimal configurations are also likely to be similar.
Parameters#
- ref_log: List of (autotvm.measure.MeasureInput, autotvm.measure.MeasureResult)
The reference log
- class tvm.autotvm.task.space.OtherOptionEntity(val)[源代码]#
The parameter entity for general option, with a detailed value
- class tvm.autotvm.task.space.OtherOptionSpace(axes, policy, **kwargs)[源代码]#
The parameter space for general option
- class tvm.autotvm.task.space.ReorderEntity(perm)[源代码]#
A reorder operation with detailed parameters that can apply to axes
Parameters#
- perm: Array of int
define the permutation
- class tvm.autotvm.task.space.ReorderSpace(axes, policy, **kwargs)[源代码]#
The parameter space for ordering an array of axes
- class tvm.autotvm.task.space.SplitEntity(size)[源代码]#
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).
- class tvm.autotvm.task.space.SplitSpace(axes, policy, **kwargs)[源代码]#
Split an axis for several times
- class tvm.autotvm.task.space.TransformSpace[源代码]#
Base class for transform space TransformSpace is the node in the computation graph of axes
备注
We can regard our schedule code as a transformation graph of axes. Starting from raw axes in the definition of te.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 all the combinations of the parameters of these op form 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.
- class tvm.autotvm.task.space.VirtualAxis(var, name=None)[源代码]#
Axis placeholder in template
Parameters#
- var: int or tvm.te.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
- tvm.autotvm.task.space.get_factors(n)[源代码]#
return all factors of an integer
Parameters#
- n: int
integer to factorize
Returns#
- factors: list
List of all factors
- tvm.autotvm.task.space.get_pow2s(n)[源代码]#
return all power-of-two numbers that are less or equal than the integer
Parameters#
- n: int
integer for reference
Returns#
- factors: list
List of all power-of-two numbers
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.
- class tvm.autotvm.task.dispatcher.ApplyConfig(config)[源代码]#
Apply a deterministic config entity for all queries.
Parameters#
- configConfigSpace or ConfigEntity
The specific configuration we care about.
- class tvm.autotvm.task.dispatcher.ApplyFixedConfig(tasks, schedule_names)[源代码]#
Apply a config of a deterministic schedule. This is used for building a single Relay operator with deterministic schedule for testing schedules at Relay level.
Parameters#
- taskslist[tvm.autotvm.task.task.Task]
List of autoTVM tasks.
- schedule_namesstr, List[str]
Name of schedules to use.
- class tvm.autotvm.task.dispatcher.ApplyGraphBest(records)[源代码]#
Load the graph level tuning optimal schedules.
The input records should be in the ascending order of node index for target operator. Usually this can be obtained with graph tuner.
This context maintains an internal counter to indicate the current node index.
- 参数:
records (str | bytes | Path | TextIOBase | Iterable[Tuple[MeasureInput, MeasureResult]])
- __init__(records)[源代码]#
Parameters#
- recordsstr or iterator of (autotvm.measure.MeasureInput, autotvm.measure.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.
- 参数:
records (str | bytes | Path | TextIOBase | Iterable[Tuple[MeasureInput, MeasureResult]])
- _query_inside(target, workload)[源代码]#
Query the context to get config from records.
Parameters#
- targetTarget
The current target
- workloadWorkload
The current workload.
Returns#
- cfgConfigSpace
The specific configuration.
- update(target, workload, cfg)[源代码]#
Update context with a specific config.
Parameters#
- target: Target
The current target
- workloadWorkload
The current workload.
- cfgConfigSpace
The specific configuration.
Note#
This interface is for cases when TVM decides to replace an operator in the graph. For example, AlterOpLayout pass (enables when opt_level = 3) replaces NCHW convolution with NCHW[x]c implementation on x86 CPUs. Thus in TOPI, we first query schedule using original NCHW workload, then update the dispatcher with the new NCHW[x]c workload. So that later on, NCHW[x]c convolution can get schedule from the dispatcher using its own workload directly.
@conv2d_alter_layout.register("cpu") def _alter_conv2d_layout(attrs, inputs, tinfo): workload = get_conv2d_workload(...) dispatch_ctx = autotvm.task.DispatchContext.current target = tvm.target.Target.current() config = dispatch_ctx.query(target, workload) # Get conv2d_NCHWc workload from config # new_workload = ... # new_inputs = ... # new_attrs = ... # Store altered operator's config dispatch_ctx.update(target, new_workload, config) return sym.contrib.conv2d_NCHWc(*new_inputs, **new_attrs)
We directly store config back because conv2d_NCHW and conv2d_NCHWc share the same schedule parameters. One can construct a new ConfigEntity if this is not the case.
- class tvm.autotvm.task.dispatcher.ApplyHistoryBest(records)[源代码]#
Apply the history best config
Parameters#
- recordsNone, Records, or iterator of Records objects, where a
Records object is a path-like object, a file-like object, or an iterator of (MeasureInput, MeasureResult).
Collection of tuning records. If multiple Records objects are passed, their contents will be merged.
- load(records)[源代码]#
Load records to this dispatch context
Parameters#
records : str, list of str, or iterator of (autotvm.measure.MeasureInput, autotvm.measure.MeasureResult)
Collection of tuning records. If multiple Records objects are passed, their contents will be merged.
- 参数:
records (str | bytes | Path | TextIOBase | Iterable[Tuple[MeasureInput, MeasureResult]] | Iterable[str | bytes | Path | TextIOBase | Iterable[Tuple[MeasureInput, MeasureResult]]])
- update(target, workload, cfg)[源代码]#
Update context with a specific config.
Parameters#
- target: Target
The current target
- workloadWorkload
The current workload.
- cfgConfigSpace
The specific configuration.
Note#
This interface is for cases when TVM decides to replace an operator in the graph. For example, AlterOpLayout pass (enables when opt_level = 3) replaces NCHW convolution with NCHW[x]c implementation on x86 CPUs. Thus in TOPI, we first query schedule using original NCHW workload, then update the dispatcher with the new NCHW[x]c workload. So that later on, NCHW[x]c convolution can get schedule from the dispatcher using its own workload directly.
@conv2d_alter_layout.register("cpu") def _alter_conv2d_layout(attrs, inputs, tinfo): workload = get_conv2d_workload(...) dispatch_ctx = autotvm.task.DispatchContext.current target = tvm.target.Target.current() config = dispatch_ctx.query(target, workload) # Get conv2d_NCHWc workload from config # new_workload = ... # new_inputs = ... # new_attrs = ... # Store altered operator's config dispatch_ctx.update(target, new_workload, config) return sym.contrib.conv2d_NCHWc(*new_inputs, **new_attrs)
We directly store config back because conv2d_NCHW and conv2d_NCHWc share the same schedule parameters. One can construct a new ConfigEntity if this is not the case.
- 参数:
records (None | str | bytes | Path | TextIOBase | Iterable[Tuple[MeasureInput, MeasureResult]] | Iterable[str | bytes | Path | TextIOBase | Iterable[Tuple[MeasureInput, MeasureResult]]])
- class tvm.autotvm.task.dispatcher.DispatchContext[源代码]#
Base class of dispatch context.
DispatchContext enables the target and workload specific dispatch mechanism for templates.
- _query_inside(target, workload)[源代码]#
Query the context to get the specific config for a template. This function only query config inside this context.
Parameters#
- target: Target
The current target
- workloadWorkload
The current workload.
Returns#
- cfgConfigSpace
The specific configuration.
- query(target, workload)[源代码]#
Query the context to get the specific config for a template. If cannot find the result inside this context, this function will query it from the upper contexts.
Parameters#
- target: Target
The current target
- workloadWorkload
The current workload.
Returns#
- cfgConfigSpace
The specific configuration.
- update(target, workload, cfg)[源代码]#
Update context with a specific config.
Parameters#
- target: Target
The current target
- workloadWorkload
The current workload.
- cfgConfigSpace
The specific configuration.
Note#
This interface is for cases when TVM decides to replace an operator in the graph. For example, AlterOpLayout pass (enables when opt_level = 3) replaces NCHW convolution with NCHW[x]c implementation on x86 CPUs. Thus in TOPI, we first query schedule using original NCHW workload, then update the dispatcher with the new NCHW[x]c workload. So that later on, NCHW[x]c convolution can get schedule from the dispatcher using its own workload directly.
@conv2d_alter_layout.register("cpu") def _alter_conv2d_layout(attrs, inputs, tinfo): workload = get_conv2d_workload(...) dispatch_ctx = autotvm.task.DispatchContext.current target = tvm.target.Target.current() config = dispatch_ctx.query(target, workload) # Get conv2d_NCHWc workload from config # new_workload = ... # new_inputs = ... # new_attrs = ... # Store altered operator's config dispatch_ctx.update(target, new_workload, config) return sym.contrib.conv2d_NCHWc(*new_inputs, **new_attrs)
We directly store config back because conv2d_NCHW and conv2d_NCHWc share the same schedule parameters. One can construct a new ConfigEntity if this is not the case.
- class tvm.autotvm.task.dispatcher.FallbackContext[源代码]#
A fallback dispatch context.
Any tunable template can be called under this context. This is the root context.
- clear_cache(target, workload)[源代码]#
Clear fallback cache. Pass the same argument as _query_inside to this function to clean the cache.
Parameters#
- target: Target
The current target
- workloadWorkload
The current workload.
- update(target, workload, cfg)[源代码]#
Update context with a specific config.
Parameters#
- target: Target
The current target
- workloadWorkload
The current workload.
- cfgConfigSpace
The specific configuration.
Note#
This interface is for cases when TVM decides to replace an operator in the graph. For example, AlterOpLayout pass (enables when opt_level = 3) replaces NCHW convolution with NCHW[x]c implementation on x86 CPUs. Thus in TOPI, we first query schedule using original NCHW workload, then update the dispatcher with the new NCHW[x]c workload. So that later on, NCHW[x]c convolution can get schedule from the dispatcher using its own workload directly.
@conv2d_alter_layout.register("cpu") def _alter_conv2d_layout(attrs, inputs, tinfo): workload = get_conv2d_workload(...) dispatch_ctx = autotvm.task.DispatchContext.current target = tvm.target.Target.current() config = dispatch_ctx.query(target, workload) # Get conv2d_NCHWc workload from config # new_workload = ... # new_inputs = ... # new_attrs = ... # Store altered operator's config dispatch_ctx.update(target, new_workload, config) return sym.contrib.conv2d_NCHWc(*new_inputs, **new_attrs)
We directly store config back because conv2d_NCHW and conv2d_NCHWc share the same schedule parameters. One can construct a new ConfigEntity if this is not the case.
- tvm.autotvm.task.dispatcher.clear_fallback_cache(target, workload)[源代码]#
Clear fallback cache. Pass the same argument as _query_inside to this function to clean the cache.
Parameters#
- target: Target
The current target
- workloadWorkload
The current workload.
Note#
This is used in alter_op_layout to clear the bad cache created before call topi compute function
Decorators for registering tunable templates to TOPI.
These decorators can make your simple implementation be able to use different configurations for different workloads. Here we directly use all arguments to the TOPI call as "workload", so make sure all the arguments (except tvm.te.Tensor) in you calls are hashable. For tvm.te.Tensor, we will serialize it to a hashable tuple.
See tvm/topi/python/topi/arm_cpu/depthwise_conv2d.py for example usage.
- class tvm.autotvm.task.topi_integration.TaskExtractEnv(allow_duplicate=False)[源代码]#
Global environment for extracting tuning tasks from graph
- add_task(task_name, args)[源代码]#
Add AutoTVM task
Parameters#
- task_name: str
AutoTVM task name.
- args: tuple
Arguments to the TOPI function.
- static get(allow_duplicate=False)[源代码]#
Get the single instance of TaskExtractEnv
Parameters#
- allow_duplicateboolean
Whether to fetch all workloads in the network, even though some of them are the same. This is useful for graph tuning.
Returns#
- env: TaskExtractEnv
The single instance of TaskExtractEnv
- tvm.autotvm.task.topi_integration.get_workload(outs, task_name=None)[源代码]#
Retrieve the workload from outputs
- tvm.autotvm.task.topi_integration.register_topi_compute(task_name, func=None)[源代码]#
Register a tunable template for a topi compute function.
The registration will wrap this topi compute to take cfg as the first argument, followed by the original argument list. It uses all its argument as workload and stores this "workload" to its final ComputeOp, which can be used to reconstruct "workload" in the following topi_schedule call.
Parameters#
- task_name: str
The AutoTVM task name
- func: None or callable
If it is None, return a decorator. If is callable, decorate this function.
Returns#
- decorator: callable
A decorator
Examples#
See tvm/topi/python/topi/arm_cpu/depthwise_conv2d.py for example usage.
- tvm.autotvm.task.topi_integration.register_topi_schedule(task_name, func=None)[源代码]#
Register a tunable template for a topi schedule function.
The registration will wrap this topi schedule to take cfg as the first argument, followed by the original argument list.
Note that this function will try to find "workload" from all the ComputeOp in the input. You can attach "workload" to your compute op by using
register_topi_compute.The task name has to be the same as that of the corresponding topi compute function.
Parameters#
- task_name: str
The AutoTVM task name
- func: None or callable
If it is None, return a decorator. If is callable, decorate this function.
Returns#
- decorator: callable
A decorator
Examples#
See tvm/topi/python/topi/arm_cpu/depthwise_conv2d.py for example usage.
tvm.autotvm.record#
Tuning record and serialization format
- tvm.autotvm.record.decode(row, protocol='json')[源代码]#
Decode encoded record string to python object
Parameters#
- rowstr
a row in the logger file
- protocolstr
log protocol, json or pickle
Returns#
- rettuple(autotvm.measure.MeasureInput, autotvm.measure.MeasureResult), or None
The tuple of input and result, or None if input uses old version log format.
- tvm.autotvm.record.encode(inp, result, protocol='json')[源代码]#
encode (MeasureInput, MeasureResult) pair to a string
Parameters#
inp: autotvm.measure.MeasureInput result: autotvm.measure.MeasureResult
pair of input/result
- protocol: str
log protocol, json or pickle
Returns#
- row: str
a row in the logger file
- tvm.autotvm.record.load_from_buffer(file)[源代码]#
Generator: load records from buffer. This is a generator that yields the records.
Parameters#
file: io.TextIOBase
Yields#
input: autotvm.measure.MeasureInput result: autotvm.measure.MeasureResult
- 参数:
file (TextIOBase)
- tvm.autotvm.record.load_from_file(filepath)[源代码]#
Generator: load records from path. This is a generator that yields the records.
Parameters#
filepath: str, bytes, or os.PathLike
Yields#
input: autotvm.measure.MeasureInput result: autotvm.measure.MeasureResult
- tvm.autotvm.record.measure_str_key(inp, include_config=True)[源代码]#
get unique str key for MeasureInput
Parameters#
- inp: autotvm.measure.MeasureInput
input for the measure
- include_config: bool, optional
whether includes config in the str key
Returns#
- key: str
The str representation of key
- tvm.autotvm.record.pick_best(in_file, out_file)[源代码]#
Pick the best entries from a file and store them to another file. This function distills the useful log entries from a large log file. If out_file already exists, the best entries from both in_file and out_file will be saved.
Parameters#
- in_file: str
The filename of input
- out_file: str or file
The filename of output