do not allow mapping to the same workload; remove important_knob_num
This commit is contained in:
yangdsh
Dana Van Aken
parent
ec67e739d8
commit
0bf2da738f
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@ -89,63 +89,6 @@ class ConfigurationRecommendation(BaseTask): # pylint: disable=abstract-method
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task_meta.save()
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def clean_knob_data(knob_matrix, knob_labels, session):
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# Filter and amend knob_matrix and knob_labels according to the tunable knobs in the session
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knob_matrix = np.array(knob_matrix)
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session_knobs = SessionKnob.objects.get_knobs_for_session(session)
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knob_cat = [k['name'] for k in session_knobs]
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if knob_cat == knob_labels:
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# Nothing to do!
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return knob_matrix, knob_labels
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LOG.info("session_knobs: %s, knob_labels: %s, missing: %s, extra: %s", len(knob_cat),
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len(knob_labels), len(set(knob_cat) - set(knob_labels)),
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len(set(knob_labels) - set(knob_cat)))
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nrows = knob_matrix.shape[0] # pylint: disable=unsubscriptable-object
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new_labels = []
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new_columns = []
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for knob in session_knobs:
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knob_name = knob['name']
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if knob_name not in knob_labels:
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# Add missing column initialized to knob's default value
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default_val = knob['default']
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try:
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if knob['vartype'] == VarType.ENUM:
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default_val = knob['enumvals'].split(',').index(default_val)
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elif knob['vartype'] == VarType.BOOL:
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default_val = str(default_val).lower() in ("on", "true", "yes", "0")
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else:
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default_val = float(default_val)
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except ValueError:
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LOG.warning("Error parsing knob '%s' default value: %s. Setting default to 0.",
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knob_name, default_val, exc_info=True)
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default_val = 0
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new_col = np.ones((nrows, 1), dtype=float) * default_val
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new_lab = knob_name
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else:
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index = knob_labels.index(knob_name)
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new_col = knob_matrix[:, index].reshape(-1, 1)
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new_lab = knob_labels[index]
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new_labels.append(new_lab)
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new_columns.append(new_col)
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new_matrix = np.hstack(new_columns).reshape(nrows, -1)
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LOG.debug("Cleaned matrix: %s, knobs (%s): %s", new_matrix.shape, len(new_labels), new_labels)
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assert new_labels == knob_cat, \
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"Expected knobs: {}\nActual knobs: {}\n".format(
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knob_cat, new_labels)
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assert new_matrix.shape == (nrows, len(knob_cat)), \
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"Expected shape: {}, Actual shape: {}".format(
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(nrows, len(knob_cat)), new_matrix.shape)
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return new_matrix, new_labels
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def clean_metric_data(metric_matrix, metric_labels, session):
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# Makes sure that all knobs in the dbms are included in the knob_matrix and knob_labels
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metric_objs = MetricCatalog.objects.filter(dbms=session.dbms)
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@ -421,7 +364,8 @@ def aggregate_target_results(aggregate_target_results_input):
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agg_data['status'] = 'good'
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# Clean knob data
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cleaned_agg_data = clean_knob_data(agg_data['X_matrix'], agg_data['X_columnlabels'], session)
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cleaned_agg_data = DataUtil.clean_knob_data(agg_data['X_matrix'],
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agg_data['X_columnlabels'], [session])
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agg_data['X_matrix'] = np.array(cleaned_agg_data[0])
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agg_data['X_columnlabels'] = np.array(cleaned_agg_data[1])
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LOG.debug("%s ~ FINAL: X_matrix=%s, X_columnlabels=%s", task_name,
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@ -581,7 +525,8 @@ def train_ddpg(train_ddpg_input):
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prev_normalized_metric_data = prev_metric_scalar.transform(prev_metric_data.reshape(1, -1))[0]
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# Clean knob data
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cleaned_knob_data = clean_knob_data(agg_data['X_matrix'], agg_data['X_columnlabels'], session)
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cleaned_knob_data = DataUtil.clean_knob_data(agg_data['X_matrix'],
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agg_data['X_columnlabels'], [session])
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knob_data = np.array(cleaned_knob_data[0])
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knob_labels = np.array(cleaned_knob_data[1])
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knob_bounds = np.vstack(DataUtil.get_knob_bounds(knob_labels.flatten(), session))
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@ -701,7 +646,8 @@ def configuration_recommendation_ddpg(recommendation_ddpg_input): # pylint: dis
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metric_data = metric_data.flatten()
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metric_scalar = MinMaxScaler().fit(metric_data.reshape(1, -1))
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normalized_metric_data = metric_scalar.transform(metric_data.reshape(1, -1))[0]
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cleaned_knob_data = clean_knob_data(agg_data['X_matrix'], agg_data['X_columnlabels'], session)
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cleaned_knob_data = DataUtil.clean_knob_data(agg_data['X_matrix'],
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agg_data['X_columnlabels'], [session])
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knob_labels = np.array(cleaned_knob_data[1]).flatten()
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knob_num = len(knob_labels)
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metric_num = len(metric_data)
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@ -730,7 +676,7 @@ def configuration_recommendation_ddpg(recommendation_ddpg_input): # pylint: dis
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return target_data_res
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def combine_workload(target_data):
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def process_training_data(target_data):
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newest_result = Result.objects.get(pk=target_data['newest_result_id'])
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latest_pipeline_run = PipelineRun.objects.get(pk=target_data['pipeline_run'])
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session = newest_result.session
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@ -752,9 +698,9 @@ def combine_workload(target_data):
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workload=mapped_workload,
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task_type=PipelineTaskType.METRIC_DATA)
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workload_metric_data = JSONUtil.loads(workload_metric_data.data)
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cleaned_workload_knob_data = clean_knob_data(workload_knob_data["data"],
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cleaned_workload_knob_data = DataUtil.clean_knob_data(workload_knob_data["data"],
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workload_knob_data["columnlabels"],
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newest_result.session)
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[newest_result.session])
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X_workload = np.array(cleaned_workload_knob_data[0])
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X_columnlabels = np.array(cleaned_workload_knob_data[1])
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y_workload = np.array(workload_metric_data['data'])
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@ -783,23 +729,6 @@ def combine_workload(target_data):
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'identical y columnlabels (sorted metric names)'),
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y_columnlabels, target_data['y_columnlabels'])
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if target_data['mapped_workload'] is not None:
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# Filter Xs by top 'IMPORTANT_KNOB_NUMBER' ranked knobs
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ranked_knobs = PipelineData.objects.get(
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pipeline_run=latest_pipeline_run,
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workload=mapped_workload,
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task_type=PipelineTaskType.RANKED_KNOBS)
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pipeline_data_knob = ranked_knobs
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pipeline_data_metric = PipelineData.objects.get(
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pipeline_run=latest_pipeline_run,
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workload=mapped_workload,
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task_type=PipelineTaskType.PRUNED_METRICS)
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ranked_knobs = JSONUtil.loads(ranked_knobs.data)[:params['IMPORTANT_KNOB_NUMBER']]
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ranked_knob_idxs = [i for i, cl in enumerate(X_columnlabels) if cl in ranked_knobs]
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X_workload = X_workload[:, ranked_knob_idxs]
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X_target = X_target[:, ranked_knob_idxs]
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X_columnlabels = X_columnlabels[ranked_knob_idxs]
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# Filter ys by current target objective metric
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target_objective = newest_result.session.target_objective
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target_obj_idx = [i for i, cl in enumerate(y_columnlabels) if cl == target_objective]
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@ -945,7 +874,7 @@ def configuration_recommendation(recommendation_input):
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X_columnlabels, X_scaler, X_scaled, y_scaled, X_max, X_min,\
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dummy_encoder, constraint_helper, pipeline_knobs,\
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pipeline_metrics = combine_workload(target_data)
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pipeline_metrics = process_training_data(target_data)
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# FIXME: we should generate more samples and use a smarter sampling technique
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num_samples = params['NUM_SAMPLES']
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@ -1108,12 +1037,10 @@ def map_workload(map_workload_input):
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workload__hardware=target_workload.hardware,
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workload__project=target_workload.project)
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# FIXME (dva): we should also compute the global (i.e., overall) ranked_knobs
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# and pruned metrics but we just use those from the first workload for now
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# FIXME (dva): we should also compute the global (i.e., overall)
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# pruned metrics but we just use those from the first workload for now
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initialized = False
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global_ranked_knobs = None
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global_pruned_metrics = None
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ranked_knob_idxs = None
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pruned_metric_idxs = None
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unique_workloads = pipeline_data.values_list('workload', flat=True).distinct()
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@ -1131,9 +1058,9 @@ def map_workload(map_workload_input):
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# Load knob & metric data for this workload
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knob_data = load_data_helper(pipeline_data, unique_workload, PipelineTaskType.KNOB_DATA)
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knob_data["data"], knob_data["columnlabels"] = clean_knob_data(knob_data["data"],
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knob_data["columnlabels"],
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newest_result.session)
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knob_data["data"], knob_data["columnlabels"] =\
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DataUtil.clean_knob_data(knob_data["data"], knob_data["columnlabels"],
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[newest_result.session])
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metric_data = load_data_helper(pipeline_data, unique_workload, PipelineTaskType.METRIC_DATA)
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X_matrix = np.array(knob_data["data"])
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y_matrix = np.array(metric_data["data"])
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@ -1141,25 +1068,17 @@ def map_workload(map_workload_input):
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assert np.array_equal(rowlabels, metric_data["rowlabels"])
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if not initialized:
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# For now set ranked knobs & pruned metrics to be those computed
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# for the first workload
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global_ranked_knobs = load_data_helper(
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pipeline_data, unique_workload,
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PipelineTaskType.RANKED_KNOBS)[:params['IMPORTANT_KNOB_NUMBER']]
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# For now set pruned metrics to be those computed for the first workload
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global_pruned_metrics = load_data_helper(
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pipeline_data, unique_workload, PipelineTaskType.PRUNED_METRICS)
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ranked_knob_idxs = [i for i in range(X_matrix.shape[1]) if X_columnlabels[
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i] in global_ranked_knobs]
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pruned_metric_idxs = [i for i in range(y_matrix.shape[1]) if y_columnlabels[
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i] in global_pruned_metrics]
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# Filter X & y columnlabels by top ranked_knobs & pruned_metrics
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X_columnlabels = X_columnlabels[ranked_knob_idxs]
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# Filter y columnlabels by pruned_metrics
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y_columnlabels = y_columnlabels[pruned_metric_idxs]
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initialized = True
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# Filter X & y matrices by top ranked_knobs & pruned_metrics
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X_matrix = X_matrix[:, ranked_knob_idxs]
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# Filter y matrices by pruned_metrics
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y_matrix = y_matrix[:, pruned_metric_idxs]
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# Combine duplicate rows (rows with same knob settings)
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@ -1172,11 +1091,11 @@ def map_workload(map_workload_input):
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'rowlabels': rowlabels,
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}
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if len(workload_data) == 0:
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if len(workload_data) < 2:
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# The background task that aggregates the data has not finished running yet
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target_data.update(mapped_workload=None, scores=None)
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LOG.debug('%s: Result = %s\n', task_name, _task_result_tostring(target_data))
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LOG.info('%s: Skipping workload mapping because no workload data is available.',
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LOG.info('%s: Skipping workload mapping because less than 2 workloads are available.',
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task_name)
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return target_data, algorithm
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@ -1194,8 +1113,8 @@ def map_workload(map_workload_input):
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del Xs
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del ys
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# Filter the target's X & y data by the ranked knobs & pruned metrics.
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X_target = target_data['X_matrix'][:, ranked_knob_idxs]
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X_target = target_data['X_matrix']
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# Filter the target's y data by the pruned metrics.
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y_target = target_data['y_matrix'][:, pruned_metric_idxs]
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# Now standardize the target's data and bin it by the deciles we just
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@ -1206,6 +1125,9 @@ def map_workload(map_workload_input):
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scores = {}
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for workload_id, workload_entry in list(workload_data.items()):
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LOG.info('%s: %s', newest_result.workload.pk, workload_id)
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if newest_result.workload.pk == workload_id:
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continue
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predictions = np.empty_like(y_target)
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X_workload = workload_entry['X_matrix']
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X_scaled = X_scaler.transform(X_workload)
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@ -156,7 +156,14 @@ def run_background_tasks():
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# PipelineData object.
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LOG.info("Ranking knobs for workload %s (use pruned metric data: %s)...",
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workload_name, KNOB_IDENT_USE_PRUNED_METRICS)
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ranked_knobs = run_knob_identification(knob_data=knob_data,
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sessions = []
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for result in wkld_results:
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if result.session not in sessions:
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sessions.append(result.session)
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rank_knob_data = copy.deepcopy(knob_data)
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rank_knob_data['data'], rank_knob_data['columnlabels'] =\
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DataUtil.clean_knob_data(knob_data['data'], knob_data['columnlabels'], sessions)
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ranked_knobs = run_knob_identification(knob_data=rank_knob_data,
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metric_data=ranked_metric_data,
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dbms=workload.dbms)
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LOG.info("Done ranking knobs for workload %s (# ranked knobs: %s).\n\n"
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@ -211,6 +211,70 @@ class DataUtil(object):
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rowlabels_unique[i] = tuple(rowlabels[dup_idxs])
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return X_unique, y_unique, rowlabels_unique
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@staticmethod
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def clean_knob_data(knob_matrix, knob_labels, sessions):
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# Filter and amend knob_matrix and knob_labels according to the tunable knobs in the session
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knob_matrix = np.array(knob_matrix)
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session_knobs = []
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knob_cat = []
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for session in sessions:
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knobs_for_this_session = SessionKnob.objects.get_knobs_for_session(session)
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for knob in knobs_for_this_session:
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if knob['name'] not in knob_cat:
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session_knobs.append(knob)
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knob_cat = [k['name'] for k in session_knobs]
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if knob_cat == knob_labels:
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# Nothing to do!
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return knob_matrix, knob_labels
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LOG.info("session_knobs: %s, knob_labels: %s, missing: %s, extra: %s", len(knob_cat),
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len(knob_labels), len(set(knob_cat) - set(knob_labels)),
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len(set(knob_labels) - set(knob_cat)))
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nrows = knob_matrix.shape[0] # pylint: disable=unsubscriptable-object
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new_labels = []
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new_columns = []
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for knob in session_knobs:
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knob_name = knob['name']
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if knob_name not in knob_labels:
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# Add missing column initialized to knob's default value
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default_val = knob['default']
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try:
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if knob['vartype'] == VarType.ENUM:
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default_val = knob['enumvals'].split(',').index(default_val)
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elif knob['vartype'] == VarType.BOOL:
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default_val = str(default_val).lower() in ("on", "true", "yes", "0")
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else:
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default_val = float(default_val)
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except ValueError:
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LOG.warning("Error parsing knob '%s' default value: %s. Setting default to 0.",
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knob_name, default_val, exc_info=True)
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default_val = 0
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new_col = np.ones((nrows, 1), dtype=float) * default_val
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new_lab = knob_name
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else:
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index = knob_labels.index(knob_name)
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new_col = knob_matrix[:, index].reshape(-1, 1)
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new_lab = knob_labels[index]
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new_labels.append(new_lab)
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new_columns.append(new_col)
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new_matrix = np.hstack(new_columns).reshape(nrows, -1)
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LOG.debug("Cleaned matrix: %s, knobs (%s): %s", new_matrix.shape,
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len(new_labels), new_labels)
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assert new_labels == knob_cat, \
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"Expected knobs: {}\nActual knobs: {}\n".format(
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knob_cat, new_labels)
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assert new_matrix.shape == (nrows, len(knob_cat)), \
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"Expected shape: {}, Actual shape: {}".format(
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(nrows, len(knob_cat)), new_matrix.shape)
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return new_matrix, new_labels
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@staticmethod
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def dummy_encoder_helper(featured_knobs, dbms):
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n_values = []
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