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xgb_model.py
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xgb_model.py
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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
# pylint: disable=invalid-name
"""Cost model based on xgboost"""
import multiprocessing
import logging
import os
from collections import defaultdict
import numpy as np
from tvm.autotvm.tuner.metric import max_curve
from .cost_model import PythonBasedModel
from ..feature import get_per_store_features_from_measure_pairs, get_per_store_features_from_states
from ..measure_record import RecordReader
xgb = None
logger = logging.getLogger("auto_scheduler")
class XGBDMatrixContext:
"""A global context to hold additional attributes of xgb.DMatrix"""
def __init__(self):
self.context_dict = defaultdict(dict)
def get(self, key, matrix, default=None):
"""
Get an attribute of a xgb.DMatrix
Parameters
----------
key: str
The name of the attribute
matrix: xgb.DMatrix
The matrix
default: Optional[Any]
The default value if the item does not exist
"""
return self.context_dict[key].get(matrix.handle.value, default)
def set(self, key, matrix, value):
"""
Set an attribute for a xgb.DMatrix
Parameters
----------
key: str
The name of the attribute
matrix: xgb.DMatrix
The matrix
value: Optional[Any]
The new value
"""
self.context_dict[key][matrix.handle.value] = value
dmatrix_context = XGBDMatrixContext()
class XGBModel(PythonBasedModel):
"""Train a XGBoost model to predict the normalized throughputs of programs.
Let the normalized throughput be the score of a program (higher is better). We predict
the (approximate) score of a program = the sum of the scores of all stages in this program.
i.e. score(P) = score_s0 + score_s1 + ... + score_sn,
where score_si is the score of Stage i in Program P.
We extract feature for each stage and let the xgboost predict the score for each stage.
We then sum up the predictions as the score of the whole program.
We use RMSE as the loss function. i.e. loss(P, y) = 1/2 * (score(P) - y)^2,
where P is the program and y is the normalized throughput according to
the ground truth (measurement).
XGBoost does not support this loss function because `score(P)` is a sum of the prediction
of several samples, so we implemented a custom loss function and call it pack-sum-rmse.
It is called "pack-sum" because we combine several samples into a "pack" and sum up
their predictions.
"""
def __init__(self, verbose_eval=25, num_warmup_sample=100, seed=None, model_file=None):
global xgb
try:
if xgb is None:
xgb = __import__("xgboost")
except ImportError:
raise ImportError(
"XGBoost is required for XGBModel. "
"Please install its python package first. "
"Help: (https://xgboost.readthedocs.io/en/latest/) "
)
self.xgb_params = {
"max_depth": 10,
"gamma": 0.001,
"min_child_weight": 0,
"eta": 0.2,
# todo(merrymercy): automatically decrease learning rate when the loss is too large
"n_gpus": 0,
"nthread": multiprocessing.cpu_count() // 2,
"verbosity": 0,
"seed": seed or 43,
"disable_default_eval_metric": 1,
}
self.bst = None
self.plan_size = 32
self.num_warmup_sample = num_warmup_sample
self.verbose_eval = verbose_eval
self.model_file = model_file
if model_file:
logger.info("XGBModel: Load pretrained model from %s...", model_file)
self.load(model_file)
super().__init__()
# cache measurement input/result pairs and extracted features
self.inputs = []
self.results = []
self.last_train_length = 0
self.inputs_feature_cache = []
def update(self, inputs, results):
"""Update the cost model according to new measurement results (training data).
XGBoost does not support incremental training, so we re-train a new model every time.
Parameters
----------
inputs : List[MeasureInput]
The measurement inputs
results : List[MeasureResult]
The measurement results
"""
if len(inputs) <= 0:
return
assert len(inputs) == len(results)
self.inputs.extend(inputs)
self.results.extend(results)
if len(self.inputs) - self.last_train_length < self.last_train_length / 5:
# Set a training threshold related to `last_train_length` to reduce the training
# overhead when there're too many logs
return
self.last_train_length = len(self.inputs)
# extract feature
n_cached = len(self.inputs_feature_cache)
features, normalized_throughputs, task_ids = get_per_store_features_from_measure_pairs(
self.inputs, self.results, skip_first_n_feature_extraction=n_cached
)
if n_cached > 0:
features = list(features)
features[:n_cached] = self.inputs_feature_cache
features = np.array(features, dtype=object)
self.inputs_feature_cache = features
dtrain = pack_sum_xgbmatrix(
features, normalized_throughputs, task_ids, normalized_throughputs
)
# train xgb model
self.bst = xgb.train(
self.xgb_params,
dtrain,
num_boost_round=10000,
obj=pack_sum_square_error,
callbacks=[
custom_callback(
stopping_rounds=50,
metric="tr-p-rmse",
fevals=[
pack_sum_rmse,
pack_sum_average_peak_score(self.plan_size),
],
evals=[(dtrain, "tr")],
maximize=False,
verbose_eval=self.verbose_eval,
)
],
)
# Update the model file if it has been set
if self.model_file:
self.save(self.model_file)
def predict(self, task, states):
"""Predict the scores of states
Parameters
----------
search_task : SearchTask
The search task of states
statse : List[State]
The input states
Returns
-------
scores: List[float]
The predicted scores for all states
"""
features = get_per_store_features_from_states(states, task)
if self.bst is not None and len(self.inputs) > self.num_warmup_sample:
dtest, pack_ids = feature_to_pack_sum_xgbmatrix(features)
raw_preds = self.bst.predict(dtest)
ret = predict_throughput_pack_sum(raw_preds, pack_ids)
else:
ret = np.random.uniform(0, 1, (len(states),))
# Predict -inf for invalid states that failed to be lowered.
for idx, feature in enumerate(features):
if feature.min() == feature.max() == 0:
ret[idx] = float("-inf")
return ret
def predict_stages(self, task, states):
"""Predict the scores of all stages in states. This is the breakdown version of `predict`.
Parameters
----------
search_task : SearchTask
The search task of states
statse : List[State]
The input states
Returns
-------
scores: List[float]
The predicted scores for all stages in all states in the packed format
Note
----
For faster data copy between c++ and python, the python part returns scores in a
single flatten array using a packed format. The c++ part then unpacks the flatten array.
The packed format is:
{
float scores[N]; // scores[i] is the score for states[i].
int n_stage_0; // the number of stages in states[0]
float stage_scores_0[[n_stage_0] // the scores for all stages in states[0]
int n_stage_1; // the number of stages in states[1]
float stage_scores_1[n_stage_1]; // the scores for all stages in states[1]
...
int n_stage_i; // the number of stages in states[i]
float stage_scores_1[n_stage_i]; // the scores for all stages in states[i]
... // untill i == N - 1
}
To implement this format, we also store int as float, so we can store all numbers
into a single float array.
"""
features = get_per_store_features_from_states(states, task)
if self.bst is not None and len(self.inputs) > self.num_warmup_sample:
dtest, pack_ids = feature_to_pack_sum_xgbmatrix(features)
raw_preds = self.bst.predict(dtest)
breakdown = predict_throughput_pack_sum(raw_preds, pack_ids)
stage_scores = [[] for _ in range(len(states))]
for pred, pack_id in zip(raw_preds, pack_ids):
stage_scores[pack_id].append(pred)
for idx, stage_score in enumerate(stage_scores):
breakdown = np.append(breakdown, len(stage_score))
breakdown = np.concatenate((breakdown, np.array(stage_score)))
else:
breakdown = np.concatenate(
(
np.random.uniform(0, 1, (len(states),)),
np.zeros(
len(states),
),
)
)
# Predict 0 for invalid states that failed to be lowered.
for idx, feature in enumerate(features):
if feature.min() == feature.max() == 0:
breakdown[idx] = float("-inf")
return breakdown
def update_from_file(self, file_name, n_lines=None):
"""Load measure records from a log file to update the cost model.
This function can be used to pre-train the cost model with history log files.
Parameters
----------
file_name: str
The filename
n_lines: Optional[int]
Only load first n lines of the log file
"""
inputs, results = RecordReader(file_name).read_lines(n_lines)
logger.info("XGBModel: Loaded %s measurement records from %s", len(inputs), file_name)
self.update(inputs, results)
def save(self, file_name: str):
"""Save the model to a file
Parameters
----------
file_name: str
The filename
"""
self.bst.save_model(file_name)
def load(self, file_name: str):
"""Load the model from a file
Parameters
----------
file_name: str
The filename
"""
if not os.path.isfile(file_name):
return
if self.bst is None:
self.bst = xgb.Booster(self.xgb_params)
self.bst.load_model(file_name)
self.num_warmup_sample = -1
def feature_to_pack_sum_xgbmatrix(xs):
"""Convert an extracted multi-stage feature vector to a xgbmatrx in pack-sum format
Parameters
----------
xs: np.ndarray
The feature vector
Returns
-------
dmatrix: xgb.DMatrix
The DMatrix
pack_ids: List[int]
pack ids information
"""
x_flatten = []
pack_ids = []
for ct, x in enumerate(xs):
for row in x:
x_flatten.append(row)
pack_ids.append(ct)
return xgb.DMatrix(np.array(x_flatten)), pack_ids
def pack_sum_xgbmatrix(xs, ys, gids=None, weights=None):
"""Convert (feature, label) pairs into a xgb matrix with pack-sum format
Parameters
----------
xs: np.ndarray
The feature vector
ys: np.ndarray
The normaizlied throughput
gids: Optional[List[int]]
Group id (task id)
weights: Optional[np.ndarray]
The weight of samples
Returns
-------
dmatrix: xgb.DMatrix
The DMatrix with pack-sum information
"""
if gids is not None:
# sort by group
indices = gids.argsort()
xs, ys = xs[indices], ys[indices]
group_sizes = np.bincount(gids)
if weights is not None:
weights = weights[indices]
else:
# assume it has only one group
group_sizes = [len(xs)]
x_flatten = []
y_flatten = []
weights_flatten = []
pack_ids = []
if weights is not None:
for ct, (x, y, w) in enumerate(zip(xs, ys, weights)):
for row in x:
x_flatten.append(row)
y_flatten.append(y)
weights_flatten.append(w)
pack_ids.append(ct)
else:
for ct, (x, y) in enumerate(zip(xs, ys)):
for row in x:
x_flatten.append(row)
y_flatten.append(y)
pack_ids.append(ct)
ret = xgb.DMatrix(np.array(x_flatten), y_flatten)
if weights is not None:
ret.set_weight(weights_flatten)
dmatrix_context.set("pack_ids", ret, np.array(pack_ids))
dmatrix_context.set("group_sizes", ret, group_sizes)
return ret
def predict_throughput_pack_sum(raw_preds, pack_ids):
"""Predict the throughputs for predictions in pack-sum format
Parameters
----------
raw_preds: np.ndarray
The raw predictions
pack_ids: List[int]
The pack id for predictions
Returns
-------
throughputs: np.ndarray
The throughput
"""
sum_pred = np.bincount(pack_ids, weights=raw_preds)
return sum_pred
def pack_sum_square_error(preds, dtrain):
"""Implement square error loss on pack-sum format as
a custom objective function for xgboost.
Parameters
----------
preds: np.ndarray
The predicitons
dtrain: xgb.DMatrix
The training set
Returns
-------
gradient: np.ndarray
hessian: np.ndarray
gradient and hessian according to the xgboost format
"""
pack_ids = dmatrix_context.get("pack_ids", dtrain)
weight = dtrain.get_weight()
sum_pred = np.bincount(pack_ids, weights=preds)
x = sum_pred[pack_ids]
y = dtrain.get_label()
gradient = x - y
hessian = np.ones_like(gradient)
if len(weight) == 0:
return gradient, hessian
return gradient * weight, hessian * weight
def pack_sum_rmse(raw_preds, labels):
"""Evaluate RMSE (rooted mean square error) in the pack-sum format
Parameters
----------
raw_preds: np.ndarray
The raw prediction
labels: xgb.DMatrix
The groud-truth label matrix
Returns
-------
name: str
score: float
The name and score of this metric
"""
pack_ids = dmatrix_context.get("pack_ids", labels)
preds = predict_throughput_pack_sum(raw_preds, pack_ids)[pack_ids]
return "p-rmse", np.sqrt(np.mean(np.square((preds - labels.get_label()))))
def pack_sum_average_peak_score(N):
"""Return the evaluation function for average-peak-score@N
Parameters
----------
N: int
The "N" in "average-peak-score@N"
Returns
-------
The evaluation function
"""
def feval(preds, labels):
"""Evaluate average-peak-score@N in the pack-sum format
Parameters
----------
raw_preds: np.ndarray
The raw prediction
labels: xgb.DMatrix
The groud-truth label matrix
Returns
-------
name: str
score: float
The name and score of this metric
"""
group_sizes = dmatrix_context.get("group_sizes", labels, [len(preds)])
pack_ids = dmatrix_context.get("pack_ids", labels)
preds = predict_throughput_pack_sum(preds, pack_ids)
labels = (
np.bincount(pack_ids, weights=labels.get_label())
/ np.unique(pack_ids, return_counts=True)[1]
)
scores = []
offset = 0
for size in group_sizes:
preds_group = preds[offset : offset + size]
labels_group = labels[offset : offset + size]
offset += size
trials = np.argsort(preds_group)[::-1][:N]
trial_scores = labels_group[trials]
curve = max_curve(trial_scores) / np.max(labels_group)
scores.append(np.mean(curve))
return "a-peak@%d" % N, np.mean(scores)
return feval
def custom_callback(
stopping_rounds,
metric,
fevals,
evals=(),
log_file=None,
maximize=False,
verbose_eval=True,
skip_every=2,
):
"""Callback function for xgboost to support multiple custom evaluation functions"""
# pylint: disable=import-outside-toplevel
from xgboost.core import EarlyStopException
from xgboost.callback import _fmt_metric
try:
from xgboost.training import aggcv
except ImportError:
from xgboost.callback import _aggcv as 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 = {}
if i % skip_every == 1:
return
##### 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 #####
if not isinstance(verbose_eval, bool) and verbose_eval and i % verbose_eval == 0:
infos = ["XGB iter: %3d" % i]
for item in eval_res:
if "null" in item[0]:
continue
infos.append("%s: %.6f" % (item[0], item[1]))
logger.debug("\t".join(infos))
if log_file:
with open(log_file, "a") as fout:
fout.write("\t".join(infos) + "\n")
##### 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:
logger.debug("XGB stopped. Best iteration: %s ", best_msg)
raise EarlyStopException(best_iteration)
return callback