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Fix several issues for nlp tasks (#380)

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* num cpu issue #378;
* temp fix for ray issue #379;
* transformers version.
This commit is contained in:
Chi Wang
2022-01-05 13:49:12 -08:00
committed by GitHub
parent 8593a3fe27
commit cd9740f022
4 changed files with 73 additions and 60 deletions
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117
flaml/automl.py
View File

@@ -45,7 +45,6 @@ from .data import (
FORECAST,
REGRESSION,
_is_nlp_task,
SUMMARIZATION,
NLG_TASKS,
)
from . import tune
@@ -322,60 +321,61 @@ class AutoMLState:
if self.time_budget is None
else self.time_budget - self.time_from_start
)
if self.resources_per_trial.get("gpu", 0) > 0:
# if self.resources_per_trial.get("gpu", 0) > 0:
def _trainable_function_wrapper(config: dict):
# def _trainable_function_wrapper(config: dict):
return_estimator, train_time = train_estimator(
X_train=sampled_X_train,
y_train=sampled_y_train,
config_dic=config,
task=self.task,
estimator_name=estimator,
n_jobs=self.n_jobs,
estimator_class=self.learner_classes.get(estimator),
budget=budget,
fit_kwargs=self.fit_kwargs,
)
return {"estimator": return_estimator, "train_time": train_time}
# return_estimator, train_time = train_estimator(
# X_train=sampled_X_train,
# y_train=sampled_y_train,
# config_dic=config,
# task=self.task,
# estimator_name=estimator,
# n_jobs=self.n_jobs,
# estimator_class=self.learner_classes.get(estimator),
# budget=budget,
# fit_kwargs=self.fit_kwargs,
# )
# return {"estimator": return_estimator, "train_time": train_time}
if estimator not in self.learner_classes:
self.learner_classes[estimator] = get_estimator_class(
self.task, estimator
)
# if estimator not in self.learner_classes:
# self.learner_classes[estimator] = get_estimator_class(
# self.task, estimator
# )
analysis = tune.run(
_trainable_function_wrapper,
config=config_w_resource,
metric="train_time",
mode="min",
resources_per_trial=self.resources_per_trial,
num_samples=1,
use_ray=True,
)
result = list(analysis.results.values())[0]
estimator, train_time = result["estimator"], result["train_time"]
# analysis = tune.run(
# _trainable_function_wrapper,
# config=config_w_resource,
# metric="train_time",
# mode="min",
# resources_per_trial=self.resources_per_trial,
# num_samples=1,
# use_ray=True,
# )
# result = list(analysis.results.values())[0]
# estimator, train_time = result["estimator"], result["train_time"]
else:
if _is_nlp_task(self.task):
use_ray = self.fit_kwargs.get("use_ray")
self.fit_kwargs["use_ray"] = False
estimator, train_time = train_estimator(
X_train=sampled_X_train,
y_train=sampled_y_train,
config_dic=config,
task=self.task,
estimator_name=estimator,
n_jobs=self.n_jobs,
estimator_class=self.learner_classes.get(estimator),
budget=budget,
fit_kwargs=self.fit_kwargs,
)
if _is_nlp_task(self.task):
if use_ray:
self.fit_kwargs["use_ray"] = use_ray
else:
del self.fit_kwargs["use_ray"]
# else:
if _is_nlp_task(self.task):
use_ray = self.fit_kwargs.get("use_ray")
self.fit_kwargs["use_ray"] = False
# TODO: limit number of GPUs
estimator, train_time = train_estimator(
X_train=sampled_X_train,
y_train=sampled_y_train,
config_dic=config,
task=self.task,
estimator_name=estimator,
n_jobs=self.n_jobs,
estimator_class=self.learner_classes.get(estimator),
budget=budget,
fit_kwargs=self.fit_kwargs,
)
if _is_nlp_task(self.task):
if use_ray is None:
del self.fit_kwargs["use_ray"]
else:
self.fit_kwargs["use_ray"] = use_ray
if sampled_weight is not None:
self.fit_kwargs["sample_weight"] = weight
return estimator, train_time
@@ -1984,11 +1984,18 @@ class AutoML(BaseEstimator):
self._use_ray = use_ray or n_concurrent_trials > 1
# use the following condition if we have an estimation of average_trial_time and average_trial_overhead
# self._use_ray = use_ray or n_concurrent_trials > ( average_trail_time + average_trial_overhead) / (average_trial_time)
self._state.resources_per_trial = (
{"cpu": int(os.cpu_count() / n_concurrent_trials), "gpu": gpu_per_trial}
if n_jobs < 0
else {"cpu": n_jobs, "gpu": gpu_per_trial}
)
if self._use_ray:
import ray
n_cpus = use_ray and ray.available_resources()["CPU"] or os.cpu_count()
self._state.resources_per_trial = (
# when using gpu, default cpu is 1 per job; otherwise, default cpu is n_cpus / n_concurrent_trials
{"cpu": max(int(n_cpus / n_concurrent_trials), 1), "gpu": gpu_per_trial}
if gpu_per_trial == 0
else {"cpu": 1, "gpu": gpu_per_trial}
if n_jobs < 0
else {"cpu": n_jobs, "gpu": gpu_per_trial}
)
self._retrain_in_budget = retrain_full == "budget" and (
eval_method == "holdout" and self._state.X_val is None
)

3
flaml/model.py
View File

@@ -447,8 +447,7 @@ class TransformersEstimator(BaseEstimator):
self._init_hpo_args(kwargs)
self._metric = kwargs["metric"]
if hasattr(self, "use_ray") is False:
self.use_ray = kwargs["use_ray"]
self.use_ray = kwargs.get("use_ray")
X_val = kwargs.get("X_val")
y_val = kwargs.get("y_val")

2
flaml/version.py
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@@ -1 +1 @@
__version__ = "0.9.3"
__version__ = "0.9.4"

11
setup.py
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@@ -55,7 +55,7 @@ setuptools.setup(
"statsmodels>=0.12.2",
"psutil==5.8.0",
"dataclasses",
"transformers",
"transformers>=4.14",
"datasets",
"torch",
"nltk",
@@ -77,7 +77,14 @@ setuptools.setup(
"vw": [
"vowpalwabbit",
],
"nlp": ["transformers", "datasets", "torch", "seqeval", "nltk", "rouge_score"],
"nlp": [
"transformers>=4.14",
"datasets",
"torch",
"seqeval",
"nltk",
"rouge_score",
],
"ts_forecast": ["prophet>=1.0.1", "statsmodels>=0.12.2"],
"forecast": ["prophet>=1.0.1", "statsmodels>=0.12.2"],
"benchmark": ["catboost>=0.26", "psutil==5.8.0", "xgboost==1.3.3"],