Update models
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Subproject commit 2b74b4dfa4a6996ab6135873c0329022a1b9626b
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Subproject commit 0a0c6a3185ac6bcec38b756f039b9ccc64b41827
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configs/qlib/workflow_config_transformer_basic_Alpha360.yaml
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87
configs/qlib/workflow_config_transformer_basic_Alpha360.yaml
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qlib_init:
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provider_uri: "~/.qlib/qlib_data/cn_data"
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region: cn
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market: &market all
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benchmark: &benchmark SH000300
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data_handler_config: &data_handler_config
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start_time: 2008-01-01
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end_time: 2020-08-01
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fit_start_time: 2008-01-01
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fit_end_time: 2014-12-31
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instruments: *market
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infer_processors:
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- class: RobustZScoreNorm
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kwargs:
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fields_group: feature
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clip_outlier: true
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- class: Fillna
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kwargs:
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fields_group: feature
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learn_processors:
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- class: DropnaLabel
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- class: CSRankNorm
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kwargs:
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fields_group: label
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label: ["Ref($close, -2) / Ref($close, -1) - 1"]
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port_analysis_config: &port_analysis_config
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strategy:
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class: TopkDropoutStrategy
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module_path: qlib.contrib.strategy.strategy
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kwargs:
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topk: 50
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n_drop: 5
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backtest:
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verbose: False
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limit_threshold: 0.095
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account: 100000000
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benchmark: *benchmark
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deal_price: close
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open_cost: 0.0005
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close_cost: 0.0015
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min_cost: 5
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task:
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model:
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class: QuantTransformer
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module_path: trade_models.quant_transformer
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kwargs:
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net_config:
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name: basic
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d_feat: 6
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stem_dim: 48
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embed_dims: [48, 48, 48, 48, 48]
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num_heads: [4, 4, 4, 4, 4]
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mlp_hidden_multipliers: [4, 4, 4, 4, 4]
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qkv_bias: True
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pos_drop: 0.1
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other_drop: 0.1
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opt_config:
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loss: mse
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GPU: 0
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dataset:
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class: DatasetH
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module_path: qlib.data.dataset
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kwargs:
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handler:
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class: Alpha360
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module_path: qlib.contrib.data.handler
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kwargs: *data_handler_config
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segments:
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train: [2008-01-01, 2014-12-31]
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valid: [2015-01-01, 2016-12-31]
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test: [2017-01-01, 2020-08-01]
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record:
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- class: SignalRecord
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module_path: qlib.workflow.record_temp
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kwargs: {}
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- class: SignalMseRecord
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module_path: qlib.contrib.workflow.record_temp
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kwargs: {}
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- class: SigAnaRecord
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module_path: qlib.workflow.record_temp
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kwargs:
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ana_long_short: False
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ann_scaler: 252
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- class: PortAnaRecord
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module_path: qlib.workflow.record_temp
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kwargs:
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config: *port_analysis_config
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@ -15,6 +15,7 @@
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# python exps/trading/baselines.py --alg TabNet #
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# #
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# python exps/trading/baselines.py --alg Transformer#
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# python exps/trading/baselines.py --alg TSF-A #
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#####################################################
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import sys
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import argparse
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@ -59,6 +60,7 @@ def retrieve_configs():
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alg2names["NAIVE-V1"] = "workflow_config_naive_v1_Alpha360.yaml"
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alg2names["NAIVE-V2"] = "workflow_config_naive_v2_Alpha360.yaml"
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alg2names["Transformer"] = "workflow_config_transformer_Alpha360.yaml"
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alg2names["TSF-A"] = "workflow_config_transformer_basic_Alpha360.yaml"
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# find the yaml paths
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alg2paths = OrderedDict()
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@ -6,7 +6,7 @@ import inspect
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import os
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import pprint
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import logging
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from copy import deepcopy
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import qlib
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from qlib.utils import init_instance_by_config
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from qlib.workflow import R
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@ -33,11 +33,14 @@ def set_log_basic_config(filename=None, format=None, level=None):
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if format is None:
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format = C.logging_config["formatters"]["logger_format"]["format"]
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# Remove all handlers associated with the root logger object.
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for handler in logging.root.handlers[:]:
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logging.root.removeHandler(handler)
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logging.basicConfig(filename=filename, format=format, level=level)
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def update_gpu(config, gpu):
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config = config.copy()
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config = deepcopy(config)
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if "task" in config and "model" in config["task"]:
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if "GPU" in config["task"]["model"]:
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config["task"]["model"]["GPU"] = gpu
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@ -59,13 +62,20 @@ def update_gpu(config, gpu):
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def update_market(config, market):
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config = config.copy()
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config = deepcopy(config.copy())
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config["market"] = market
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config["data_handler_config"]["instruments"] = market
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return config
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def run_exp(task_config, dataset, experiment_name, recorder_name, uri):
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def run_exp(
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task_config,
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dataset,
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experiment_name,
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recorder_name,
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uri,
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model_obj_name="model.pkl",
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):
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model = init_instance_by_config(task_config["model"])
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model_fit_kwargs = dict(dataset=dataset)
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@ -80,6 +90,7 @@ def run_exp(task_config, dataset, experiment_name, recorder_name, uri):
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# Setup log
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recorder_root_dir = R.get_recorder().get_local_dir()
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log_file = os.path.join(recorder_root_dir, "{:}.log".format(experiment_name))
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set_log_basic_config(log_file)
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logger = get_module_logger("q.run_exp")
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logger.info("task_config::\n{:}".format(pprint.pformat(task_config, indent=2)))
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@ -87,20 +98,29 @@ def run_exp(task_config, dataset, experiment_name, recorder_name, uri):
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logger.info("dataset={:}".format(dataset))
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# Train model
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R.log_params(**flatten_dict(task_config))
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if "save_path" in inspect.getfullargspec(model.fit).args:
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model_fit_kwargs["save_path"] = os.path.join(recorder_root_dir, "model.ckp")
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elif "save_dir" in inspect.getfullargspec(model.fit).args:
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model_fit_kwargs["save_dir"] = os.path.join(recorder_root_dir, "model-ckps")
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model.fit(**model_fit_kwargs)
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try:
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model = R.load_object(model_obj_name)
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logger.info("[Find existing object from {:}]".format(model_obj_name))
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except OSError:
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R.log_params(**flatten_dict(task_config))
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if "save_path" in inspect.getfullargspec(model.fit).args:
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model_fit_kwargs["save_path"] = os.path.join(
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recorder_root_dir, "model.ckp"
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)
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elif "save_dir" in inspect.getfullargspec(model.fit).args:
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model_fit_kwargs["save_dir"] = os.path.join(
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recorder_root_dir, "model-ckps"
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)
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model.fit(**model_fit_kwargs)
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R.save_objects(**{model_obj_name: model})
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except:
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raise ValueError("Something wrong.")
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# Get the recorder
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recorder = R.get_recorder()
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R.save_objects(**{"model.pkl": model})
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# Generate records: prediction, backtest, and analysis
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import pdb; pdb.set_trace()
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for record in task_config["record"]:
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record = record.copy()
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record = deepcopy(record)
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if record["class"] == "SignalRecord":
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srconf = {"model": model, "dataset": dataset, "recorder": recorder}
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record["kwargs"].update(srconf)
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@ -193,19 +193,15 @@ def get_transformer(config):
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raise ValueError("Invalid Configuration: {:}".format(config))
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name = config.get("name", "basic")
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if name == "basic":
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model = TransformerModel(
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model = SuperTransformer(
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d_feat=config.get("d_feat"),
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embed_dim=config.get("embed_dim"),
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depth=config.get("depth"),
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stem_dim=config.get("stem_dim"),
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embed_dims=config.get("embed_dims"),
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num_heads=config.get("num_heads"),
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mlp_ratio=config.get("mlp_ratio"),
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mlp_hidden_multipliers=config.get("mlp_hidden_multipliers"),
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qkv_bias=config.get("qkv_bias"),
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qk_scale=config.get("qkv_scale"),
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pos_drop=config.get("pos_drop"),
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mlp_drop_rate=config.get("mlp_drop_rate"),
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attn_drop_rate=config.get("attn_drop_rate"),
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drop_path_rate=config.get("drop_path_rate"),
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norm_layer=config.get("norm_layer", None),
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other_drop=config.get("other_drop"),
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)
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else:
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raise ValueError("Unknown model name: {:}".format(name))
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@ -14,6 +14,13 @@ IntSpaceType = Union[int, spaces.Integer, spaces.Categorical]
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BoolSpaceType = Union[bool, spaces.Categorical]
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class LayerOrder(Enum):
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"""This class defines the enumerations for order of operation in a residual or normalization-based layer."""
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PreNorm = "pre-norm"
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PostNorm = "post-norm"
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class SuperRunMode(Enum):
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"""This class defines the enumerations for Super Model Running Mode."""
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@ -15,6 +15,7 @@ import torch.nn.functional as F
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import spaces
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from .super_module import IntSpaceType
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from .super_module import BoolSpaceType
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from .super_module import LayerOrder
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from .super_module import SuperModule
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from .super_linear import SuperMLPv2
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from .super_norm import SuperLayerNorm1D
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@ -30,7 +31,8 @@ class SuperTransformerEncoderLayer(SuperModule):
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- PyTorch Implementation: https://pytorch.org/docs/stable/_modules/torch/nn/modules/transformer.html#TransformerEncoderLayer
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Details:
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MHA -> residual -> norm -> MLP -> residual -> norm
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the original post-norm version: MHA -> residual -> norm -> MLP -> residual -> norm
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the pre-norm version: norm -> MHA -> residual -> norm -> MLP -> residual
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"""
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def __init__(
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@ -42,9 +44,10 @@ class SuperTransformerEncoderLayer(SuperModule):
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mlp_hidden_multiplier: IntSpaceType = 4,
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drop: Optional[float] = None,
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act_layer: Callable[[], nn.Module] = nn.GELU,
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order: LayerOrder = LayerOrder.PreNorm,
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):
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super(SuperTransformerEncoderLayer, self).__init__()
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self.mha = SuperAttention(
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mha = SuperAttention(
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input_dim,
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input_dim,
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num_heads=num_heads,
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@ -52,17 +55,33 @@ class SuperTransformerEncoderLayer(SuperModule):
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attn_drop=drop,
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proj_drop=drop,
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)
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self.drop1 = nn.Dropout(drop or 0.0)
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self.norm1 = SuperLayerNorm1D(input_dim)
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self.mlp = SuperMLPv2(
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drop1 = nn.Dropout(drop or 0.0)
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norm1 = SuperLayerNorm1D(input_dim)
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mlp = SuperMLPv2(
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input_dim,
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hidden_multiplier=mlp_hidden_multiplier,
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out_features=output_dim,
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act_layer=act_layer,
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drop=drop,
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)
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self.drop2 = nn.Dropout(drop or 0.0)
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self.norm2 = SuperLayerNorm1D(output_dim)
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drop2 = nn.Dropout(drop or 0.0)
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norm2 = SuperLayerNorm1D(output_dim)
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if order is LayerOrder.PreNorm:
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self.norm1 = norm1
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self.mha = mha
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self.drop1 = drop1
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self.norm2 = norm2
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self.mlp = mlp
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self.drop2 = drop2
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elif order is LayerOrder.PostNoem:
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self.mha = mha
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self.drop1 = drop1
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self.norm1 = norm1
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self.mlp = mlp
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self.drop2 = drop2
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self.norm2 = norm2
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else:
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raise ValueError("Unknown order: {:}".format(order))
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@property
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def abstract_search_space(self):
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@ -89,12 +108,18 @@ class SuperTransformerEncoderLayer(SuperModule):
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return self.forward_raw(input)
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def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
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# multi-head attention
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x = self.mha(input)
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x = x + self.drop1(x)
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x = self.norm1(x)
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# feed-forward layer
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x = self.mlp(x)
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x = x + self.drop2(x)
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x = self.norm2(x)
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if order is LayerOrder.PreNorm:
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x = self.norm1(input)
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x = x + self.drop1(self.mha(x))
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x = self.norm2(x)
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x = x + self.drop2(self.mlp(x))
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elif order is LayerOrder.PostNoem:
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# multi-head attention
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x = x + self.drop1(self.mha(input))
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x = self.norm1(x)
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# feed-forward layer
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x = x + self.drop2(self.mlp(x))
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x = self.norm2(x)
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else:
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raise ValueError("Unknown order: {:}".format(order))
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return x
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