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[Ansor][AutoTVM v2.0] Phase 1: feature extraction for cost models #6190

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122 changes: 122 additions & 0 deletions include/tvm/auto_scheduler/feature.h
Original file line number Diff line number Diff line change
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/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
* KIND, either express or implied. See the License for the
* specific language governing permissions and limitations
* under the License.
*/

/*!
* \file auto_scheduler/feature.h
* \brief Feature extraction for the cost model.
* We extract one feature vector per BufferStoreNode statement in a TIR Stmt,
* so we call this feature as "Per Store" feature.
* The cost model also does prediction for each BufferStoreNode statement and aggregates
* the predictions as the whole score for a TVM IR (Stmt).
*
* The feature specification is defined by `src/auto_scheduler/feature.cc:: FeatureSet`
*/

#ifndef TVM_AUTO_SCHEDULER_FEATURE_H_
#define TVM_AUTO_SCHEDULER_FEATURE_H_

#include <tvm/auto_scheduler/compute_dag.h>
#include <tvm/auto_scheduler/measure.h>

#include <string>
#include <vector>

namespace tvm {
namespace auto_scheduler {

/*!
* \brief Get per-store feature from a TIR Stmt
* \param stmt The input lowered TIR statement
* \param cache_line_size The size of cache line in bytes
* \param max_n_bufs The maximum number of extracted buffers for one statement
* \param ret The returned feature vector
*/
void GetPerStoreFeature(const Stmt& stmt, int cache_line_size, int max_n_bufs,
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std::vector<float>* ret);

/*
* \brief Get the names of elements in the feature vector. Use this for debug and inspection.
* \param max_n_bufs The maximum number of extracted buffers for one statement
* \param ret The returned names.
*/
void GetPerStoreFeatureName(int max_n_bufs, std::vector<std::string>* ret);

/*!
* \brief Get per-store feature from states of the same task
* \param states The input states
* \param task The same search task for all states
* \param skip_first_n_feature_extraction Skip feature extraction for the first n states
* \param max_n_bufs The maximum number of extracted buffers for one statement
* \param features The returned feature vector. The innermost vector contains the
* feature vectors for all BufferStoreNode statements
*/
void GetPerStoreFeaturesFromStates(const Array<State>& states, const SearchTask& task,
int skip_first_n_feature_extraction, int max_n_bufs,
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std::vector<std::vector<float> >* features);

/*!
* \brief Get per-store feature from states of different tasks
* \param states The input states
* \param tasks The search tasks corresponding to the input states
* \param skip_first_n_feature_extraction Skip feature extraction for the first n states
* \param max_n_bufs The maximum number of extracted buffers for one statement
* \param features The returned feature vector. The innermost vector contains the
* feature vectors for all BufferStoreNode statements
*/
void GetPerStoreFeaturesFromStates(const Array<State>& states, const std::vector<SearchTask>& tasks,
int skip_first_n_feature_extraction, int max_n_bufs,
std::vector<std::vector<float> >* features);

/*!
* \brief Get per-store features from a log file
* \param filename The name of log file
* \param max_lines Only read the first n lines of the file
* \param max_n_bufs The maximum number of extracted buffers for one statement
* \param features The returned feature vector. The innermost vector contains the
* feature vectors for all BufferStoreNode statements
* \param normalized_throughputs The normalized throughputs for all states
* \param task_ids The task ids for all states
*/
void GetPerStoreFeaturesFromFile(const std::string& filename, int max_lines, int max_n_bufs,
std::vector<std::vector<float> >* features,
std::vector<float>* normalized_throughputs,
std::vector<int>* task_ids);

/*!
* \brief Get per-store features from measurement input/result pairs
* \param inputs The meaurement inputs
* \param results The measurement results
* \param skip_first_n_feature_extraction Skip feature extraction for the first n meaurement pairs
* \param max_n_bufs The maximum number of extracted buffers for one statement
* \param features The returned feature vector. The innermost vector contains the
* feature vectors for all BufferStoreNode statements
* \param normalized_throughputs The normalized throughputs for all states
* \param task_ids The task ids for all states
*/
void GetPerStoreFeaturesFromMeasurePairs(const Array<MeasureInput>& inputs,
const Array<MeasureResult>& results,
int skip_first_n_feature_extraction, int max_n_bufs,
std::vector<std::vector<float> >* features,
std::vector<float>* normalized_throughputs,
std::vector<int>* task_ids);

} // namespace auto_scheduler
} // namespace tvm

#endif // TVM_AUTO_SCHEDULER_FEATURE_H_
1 change: 1 addition & 0 deletions python/tvm/auto_scheduler/__init__.py
Original file line number Diff line number Diff line change
Expand Up @@ -23,6 +23,7 @@
from . import loop_state
from . import utils
from . import workload_registry
from . import feature

# Shortcut
from .auto_schedule import SearchTask, TuningOptions, HardwareParams, \
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242 changes: 242 additions & 0 deletions python/tvm/auto_scheduler/feature.py
Original file line number Diff line number Diff line change
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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.

""""
Python API for Feature extraction. The extracted features vector are used by cost models.

We extract one feature vector per BufferStoreNode statement in a TIR Stmt,
so we call this feature as "Per Store" feature.
The cost model also does prediction for each BufferStoreNode statement and aggregates
the predicted score of each BufferStoreNode as the score of a TIR Stmt.

The feature specification is defined by `src/auto_scheduler/feature.cc::FeatureSet`
"""

from typing import List, Tuple, Union, Optional
import struct

import numpy as np

from .loop_state import State, StateObject
from .measure import MeasureInput, MeasureResult
from . import _ffi_api

# The maximum number of extracted buffers for one statement
DEFAULT_MAX_N_BUFS = 5

# The length of the feature vector
DEFAULT_FEATURE_VEC_LEN = 164

# The size of int and float in bytes
SIZE_OF_INT32 = 4
SIZE_OF_FLOAT32 = 4

def unpack_feature(byte_arr: bytearray) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
"""Unpack the flatten feature (in byte array format) from c++

Parameters
----------
byte_arr: bytearray
The two-dimensional feature vector in serialized byte array format

Returns
-------
features: np.ndarray
Feature vectors
normalized_throughputs: np.ndarray
Normalized throughputs
task_ids: np.ndarray
Task ids
"""

# The format for n records is:
# {
# int n;
# int[n+2] sizes
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nitpick on the doc

Suggested change
# int[n+2] sizes
# int sizes[0]
# ...
# int sizes[n + 1]

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Personally, I think junrushao1994's suggestion is clearer than current comment convention.Since I think "float sizes[n + 1]" illustrates the format semantics more precisely than "float[sizes[0]]".

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@merrymercy merrymercy Aug 12, 2020

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I won't take either of your suggestions. I use int[x] variable to denote an array of x integer values with the name variable.

@junrushao1994 's suggestion is wrong because int size[n] has another meaning of n+1 integers, but here we actually mean nth integer.
@yangjunpro 's suggestion is also wrong. It makes sizes become the name of the filed, but actually size only specifies the size.


# float[sizes[0]] feature for record 1
# float[sizes[1]] feature for record 2
# ... feature for record i...
# float[sizes[n-1]] feature for record n

# float[sizes[n]] normalized throughput for n records
# int[sizes[n+1]] task id for n records
# }

vec_len = DEFAULT_FEATURE_VEC_LEN

# unpack sizes
offset = 0
n = struct.unpack_from("1i", byte_arr, offset=offset)[0]
offset += SIZE_OF_INT32

sizes = struct.unpack_from("%di" % (n+2), byte_arr, offset=offset)
offset += SIZE_OF_INT32 * (n+2)

# unpack features
features = []
for size in sizes[:-2]:
row = []

# Now, we need to unpack the feature for multiple statements.
# The format is:
# {
# int n_stmts
# float[n_stmt][vec_len] feature_vecs
# }
# where vec_len can be calculated by `(size - 1) / n_stmts`

if size == 0:
# failed during lowering
features.append(np.zeros((1, vec_len)))
else:
n_stmts = struct.unpack_from("f", byte_arr, offset=offset)
offset += SIZE_OF_FLOAT32

n_stmts = int(n_stmts[0] + 0.5)
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perhaps add a comment here, like "for avoiding rounding error"? btw, why we use a float for integer?

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@merrymercy merrymercy Aug 11, 2020

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Some of them are int while the others are float. I want to store all of them in a single array, but we do not have union in tvm::Object. So I use a single float array to store both int and float

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+1, how about store the float array and the n_stmts separately? By doing this we may need to add extra code, but I think the program semantic is clearer.

tmp_vec_len = (size - 1) // n_stmts
assert tmp_vec_len == vec_len, "The lenght of feature vector is wrong. " \
"Expected %d but got %d." % (vec_len, tmp_vec_len)
assert tmp_vec_len * n_stmts == size - 1
for _ in range(n_stmts):
x = struct.unpack_from("%df" % vec_len, byte_arr, offset=offset)
offset += vec_len * SIZE_OF_FLOAT32
row.append(x)

features.append(np.array(row))

# unpack normalized_throughputs
m = sizes[-2]
normalized_throughputs = struct.unpack_from("%df" % m, byte_arr, offset=offset)
offset += m * SIZE_OF_INT32

# unpack task_ids
m = sizes[-1]
task_ids = struct.unpack_from("%di" % m, byte_arr, offset=offset)
offset += m * SIZE_OF_INT32

assert offset == len(byte_arr), "%d vs %d" % (offset, len(byte_arr))
return np.array(features, dtype=object), np.array(normalized_throughputs), np.array(task_ids)


def get_per_store_features_from_file(filename: str,
max_lines: int,
max_n_bufs: Optional[int] = None) \
-> Tuple[np.ndarray, np.ndarray, np.ndarray]:
"""Get per_store features from a log file

Parameters
----------
filename: str
The input filename
max_lines: int
Only extract the first n lines of the file
max_n_bufs: int
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The maximum number of extracted buffers for one statement

Returns
-------
features: np.ndarray
Feature vectors
normalized_throughputs: np.ndarray
Normalized throughputs
task_ids: np.ndarray
Task ids
"""
byte_arr = _ffi_api.GetPerStoreFeaturesFromFile(
filename, max_lines, max_n_bufs or DEFAULT_MAX_N_BUFS)
return unpack_feature(byte_arr)


def get_per_store_features_from_measure_pairs(inputs: List[MeasureInput],
results: List[MeasureResult],
skip_first_n_feature_extraction: int = 0,
max_n_bufs: Optional[int] = None) \
-> Tuple[np.ndarray, np.ndarray, np.ndarray]:
"""Get per_store features from measurement input/result pairs

Parameters
----------
inputs: List[MeasureInput]
The measure inputs
results: List[MeasureResult]
The measure results
skip_first_n_feature_extraction: int
Skip feature extraction for the first n states
max_n_bufs: int
The maximum number of extracted buffers for one statement

Returns
-------
features: np.ndarray
Feature vectors
normalized_throughputs: np.ndarray
Normalized throughputs
task_ids: np.ndarray
Task ids
"""
byte_arr = _ffi_api.GetPerStoreFeaturesFromMeasurePairs(
inputs, results, skip_first_n_feature_extraction, max_n_bufs or DEFAULT_MAX_N_BUFS)
return unpack_feature(byte_arr)


def get_per_store_features_from_states(states: List[Union[State, StateObject]],
task: "SearchTask",
max_n_bufs: Optional[int] = None) -> List[np.ndarray]:
"""Get per_store features from measurement input/result pairs

Parameters
----------
states: List[Union[State, StateObject]]
The input states
task: SearchTask
The search task of the input states
max_n_bufs: int
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The maximum number of extracted buffers for one statement

Returns
-------
features: np.ndarray
Feature vectors
normalized_throughputs: np.ndarray
Normalized throughputs
task_ids: np.ndarray
Task ids
"""
if isinstance(states[0], State):
state_objects = [s.state_object for s in states]
elif isinstance(states[0], StateObject):
state_objects = states
byte_arr = _ffi_api.GetPerStoreFeaturesFromStates(
state_objects, task, max_n_bufs or DEFAULT_MAX_N_BUFS)
return unpack_feature(byte_arr)[0]


def get_per_store_feature_names(max_n_bufs: Optional[int] = None) -> List[str]:
"""Get the name of every element in the feature vector. Use this for debug and inspection.

Parameters
----------
max_n_bufs: int
The maximum number of extracted buffers for one statement

Returns
-------
names: List[str]
The names of elements in the flatten feature vector
"""
return _ffi_api.GetPerStoreFeatureNames(max_n_bufs or DEFAULT_MAX_N_BUFS)
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