def run_experiments_seed(domain="restaurants", gpu=-1, patience=10, cosinelr=False, fullsimplify=True, batsize=50, smoothing=0.2, dropout=.1, numlayers=3, numheads=12, hdim=768, useall=False, domainstart=False, nopretrain=False, numbeam=1, onlyabstract=False, uselexicon=False): ranges = { "lr": [0.0001], "ftlr": [0.0001], "enclrmul": [0.1], "warmup": [2], "epochs": [100], "pretrainepochs": [100], "numheads": [numheads], "numlayers": [numlayers], "dropout": [dropout], "smoothing": [smoothing], "hdim": [hdim], "numbeam": [numbeam], "batsize": [batsize], "seed": [12345678, 65748390, 98387670, 23655798, 66453829], # TODO: add more later } p = __file__ + f".{domain}" def check_config(x): effectiveenclr = x["enclrmul"] * x["lr"] if effectiveenclr < 0.000005: return False dimperhead = x["hdim"] / x["numheads"] if dimperhead < 20 or dimperhead > 100: return False return True q.run_experiments(run, ranges, path_prefix=p, check_config=check_config, domain=domain, fullsimplify=fullsimplify, gpu=gpu, patience=patience, cosinelr=cosinelr, domainstart=domainstart, useall=useall, uselexicon=uselexicon, nopretrain=nopretrain, onlyabstract=onlyabstract)
def run_experiments(domain="restaurants", gpu=-1, patience=10, cosinelr=False, mincoverage=2, fullsimplify=True, uselexicon=False): ranges = { "lr": [0.0001, 0.00001], #[0.001, 0.0001, 0.00001], "ftlr": [0.00003], "enclrmul": [1., 0.1], #[1., 0.1, 0.01], "warmup": [2], "epochs": [100], #[50, 100], "pretrainepochs": [100], "numheads": [8, 12, 16], "numlayers": [3, 6, 9], "dropout": [.1], "hdim": [768, 960], #[192, 384, 768, 960], "seed": [12345678], #, 98387670, 23655798, 66453829], # TODO: add more later } p = __file__ + f".{domain}" def check_config(x): effectiveenclr = x["enclrmul"] * x["lr"] if effectiveenclr < 0.00001: return False dimperhead = x["hdim"] / x["numheads"] if dimperhead < 20 or dimperhead > 100: return False return True q.run_experiments(run, ranges, path_prefix=p, check_config=check_config, domain=domain, fullsimplify=fullsimplify, uselexicon=uselexicon, gpu=gpu, patience=patience, cosinelr=cosinelr, mincoverage=mincoverage)
def run_experiments_seed(domain="restaurants", hdim=-1, dropout=-1., numlayers=-1, numheads=-1, gpu=-1): ranges = { "lr": [0.0001], "enclrmul": [0.1], "warmup": [0], "epochs": [50], "numheads": [16], "numlayers": [3], "dropout": [.1], "hdim": [960], "cosinelr": [True], "seed": [12345678, 65748390, 98387670, 23655798, 66453829], # TODO: add more later } if hdim > 0: ranges["hdim"] = [hdim] if dropout > 0: ranges["dropout"] = [dropout] if numlayers > 0: ranges["numlayers"] = [numlayers] if numheads > 0: ranges["numheads"] = [numheads] p = __file__ + f".{domain}" def check_config(x): effectiveenclr = x["enclrmul"] * x["lr"] if effectiveenclr < 0.000005: return False dimperhead = x["hdim"] / x["numheads"] if dimperhead < 20 or dimperhead > 100: return False return True q.run_experiments(run, ranges, path_prefix=p, check_config=check_config, domain=domain, gpu=gpu, trainonvalid=False)
def run_experiments(domain="restaurants", gpu=-1): ranges = { "lr": [0.0001, 0.00001], #[0.001, 0.0001, 0.00001], "enclrmul": [1., 0.1], #[1., 0.1, 0.01], "warmup": [2], "epochs": [100], #[50, 100], "numheads": [8, 12, 16], "numlayers": [3, 6, 9], "dropout": [.1, .2], "hdim": [384, 768, 960], #[192, 384, 768, 960], "seed": [12345678 ], #, 98387670, 23655798, 66453829], # TODO: add more later "cosinelr": [True], } ranges = { "lr": [0.0001], "enclrmul": [0.1], "warmup": [0], "epochs": [50, 100, 75], "numheads": [16], "numlayers": [3], "dropout": [.1], "hdim": [960], "cosinelr": [True], "seed": [12345678], # TODO: add more later } p = __file__ + f".{domain}" def check_config(x): effectiveenclr = x["enclrmul"] * x["lr"] if effectiveenclr < 0.00001: return False dimperhead = x["hdim"] / x["numheads"] if dimperhead < 20 or dimperhead > 100: return False return True q.run_experiments(run, ranges, path_prefix=p, check_config=check_config, domain=domain, gpu=gpu)
def run_experiments_seed(domain="default", gpu=-1, patience=10, cosinelr=False, fullsimplify=True, batsize=50, smoothing=0.2, dropout=.1, decoderdropout=0.5, numlayers=3, numheads=12, hdim=768, pretrainbatsize=100, resetmode="none", mincoverage=2, nopretrain=False, numbeam=1, onlyabstract=False, pretrainsetting="all", finetunesetting="min", epochs=67, pretrainepochs=60, minpretrainepochs=10): ranges = { "domain": ["recipes", "restaurants", "blocks", "calendar", "housing", "publications"], "lr": [0.0001], "ftlr": [0.0001], "enclrmul": [0.1], "warmup": [2], "epochs": [epochs], "pretrainepochs": [pretrainepochs], "numheads": [numheads], "numlayers": [numlayers], "dropout": [dropout], "decoderdropout": [decoderdropout], "smoothing": [smoothing], "hdim": [hdim], "numbeam": [numbeam], "batsize": [batsize], "seed": [12345678, 65748390, 98387670, 23655798, 66453829], # TODO: add more later } p = __file__ + f".{domain}" if domain != "default": ranges["domain"] = [domain] def check_config(x): effectiveenclr = x["enclrmul"] * x["lr"] if effectiveenclr < 0.000005: return False dimperhead = x["hdim"] / x["numheads"] if dimperhead < 20 or dimperhead > 100: return False return True q.run_experiments(run, ranges, path_prefix=p, check_config=check_config, mincoverage=mincoverage, domain=domain, fullsimplify=fullsimplify, resetmode=resetmode, gpu=gpu, patience=patience, cosinelr=cosinelr, pretrainbatsize=pretrainbatsize, pretrainsetting=pretrainsetting, finetunesetting=finetunesetting, nopretrain=nopretrain, onlyabstract=onlyabstract, minpretrainepochs=minpretrainepochs)
def run_experiments(domain="restaurants", gpu=-1, patience=10, cosinelr=False, ptN=3000, datatemp=.33): ranges = { "lr": [0.0001], "ptlr": [0.0001], "enclrmul": [1.], "warmup": [1], "ptwarmup": [5], "epochs": [100], "ptepochs": [100], "ptbatsize": [120], "numheads": [16], "numlayers": [3], "dropout": [.1], "hdim": [960], "tokenmaskp": [0., .2], "spanmaskp": [0., .2], "treemaskp": [0., .2], "seed": [12345678], # TODO: add more later } p = __file__ + f".{domain}" def check_config(x): effectiveenclr = x["enclrmul"] * x["lr"] if effectiveenclr < 0.00001: return False dimperhead = x["hdim"] / x["numheads"] if dimperhead < 20 or dimperhead > 100: return False if x["tokenmaskp"] + x["spanmaskp"] + x["treemaskp"] > .2: return False if x["tokenmaskp"] + x["spanmaskp"] + x["treemaskp"] == .0: return False return True q.run_experiments(run, ranges, path_prefix=p, check_config=check_config, domain=domain, gpu=gpu, patience=patience, cosinelr=cosinelr, ptN=ptN, datatemp=datatemp)
def run_experiments_seed(domain="restaurants", gpu=-1, patience=5, cosinelr=False,): ranges = { "lr": [0.0001], "enclrmul": [0.1], "warmup": [1], "epochs": [75], "numheads": [16], "numlayers": [3], "dropout": [.1], "hdim": [960], "seed": [12345678, 65748390, 98387670, 23655798, 66453829], # TODO: add more later } p = __file__ + f".{domain}" def check_config(x): effectiveenclr = x["enclrmul"] * x["lr"] if effectiveenclr < 0.000005: return False dimperhead = x["hdim"] / x["numheads"] if dimperhead < 20 or dimperhead > 100: return False return True q.run_experiments(run, ranges, path_prefix=p, check_config=check_config, domain=domain, gpu=gpu, patience=patience, cosinelr=cosinelr)
def _run_experiments(domain="restaurants", gpu=-1, patience=5, cosinelr=False,): ranges = { "lr": [0.001, 0.0001, 0.00001], "enclrmul": [1., 0.1, 0.01], "warmup": [0, 2], "epochs": [50, 100], "numheads": [8, 12, 16], "numlayers": [3, 6, 9], "dropout": [.1, .05, .2], "hdim": [192, 384, 768, 960], "seed": [12345678], # TODO: add more later } p = __file__ + f".{domain}" def check_config(x): effectiveenclr = x["enclrmul"] * x["lr"] if effectiveenclr < 0.00001: return False dimperhead = x["hdim"] / x["numheads"] if dimperhead < 20 or dimperhead > 100: return False return True q.run_experiments(run, ranges, path_prefix=p, check_config=check_config, domain=domain, gpu=gpu, patience=patience, cosinelr=cosinelr)
def run_experiments_seed(domain="restaurants", lr=-1., batsize=-1, patience=-1, enclrmul=-1., hdim=-1, dropout=-1., encdropout=-1., numlayers=-1, numheads=-1, gpu=-1, epochs=-1, trainonvalid=False, cosinelr=False): ranges = { "lr": [0.00005], "batsize": [8], "patience": [14], "enclrmul": [0.1], "warmup": [0], "epochs": [50], "numheads": [12], "numlayers": [8], "dropout": [.2], "encdropout": [.1], "hdim": [768], "cosinelr": [cosinelr], "seed": [12345678, 65748390, 98387670], # TODO: add more later } if lr >= 0: ranges["lr"] = [lr] if batsize >= 0: ranges["batsize"] = [batsize] if patience >= 0: ranges["patience"] = [patience] if hdim >= 0: ranges["hdim"] = [hdim] if dropout >= 0: ranges["dropout"] = [dropout] if encdropout >= 0.: ranges["encdropout"] = [encdropout] if numlayers >= 0: ranges["numlayers"] = [numlayers] if numheads >= 0: ranges["numheads"] = [numheads] if enclrmul >= 0: ranges["enclrmul"] = [enclrmul] if epochs >= 0: ranges["epochs"] = [epochs] p = __file__ + f".{domain}" def check_config(x): effectiveenclr = x["enclrmul"] * x["lr"] # if effectiveenclr < 0.000005: # return False dimperhead = x["hdim"] / x["numheads"] if dimperhead < 20 or dimperhead > 100: return False return True q.run_experiments(run, ranges, path_prefix=p, check_config=check_config, domain=domain, gpu=gpu, trainonvalid=trainonvalid)
def run_experiments_seed(sourcelang="en", supportlang="en", testlang="en", lr=-1., batsize=-1, patience=-1, enclrmul=-1., hdim=-1, dropout=-1., dropoutdec=-1., numlayers=-1, numheads=-1, gpu=-1, epochs=-1, smoothing=0., numbeam=1, trainonvalid=False, cosinelr=False, statesimweight=0., probsimweight=0., projmode="simple", seed=-1): ranges = { "lr": [0.0001], "batsize": [20], "patience": [5], "enclrmul": [0.1], "warmup": [0], "epochs": [50], "numheads": [12], "numlayers": [5], "dropout": [.1], "dropoutdec": [.1], "hdim": [768], "cosinelr": [cosinelr], "seed": [12345678, 65748390, 98387670, 23655798, 66453829], # TODO: add more later } if lr >= 0: ranges["lr"] = [lr] if batsize >= 0: ranges["batsize"] = [batsize] if patience >= 0: ranges["patience"] = [patience] if hdim >= 0: ranges["hdim"] = [hdim] if dropout >= 0: ranges["dropout"] = [dropout] if dropoutdec >= 0: ranges["dropoutdec"] = [dropoutdec] if numlayers >= 0: ranges["numlayers"] = [numlayers] if numheads >= 0: ranges["numheads"] = [numheads] if enclrmul >= 0: ranges["enclrmul"] = [enclrmul] if epochs >= 0: ranges["epochs"] = [epochs] if seed >= 0: ranges["seed"] = [seed] p = __file__ + f".{sourcelang}-{supportlang}-{testlang}" def check_config(x): effectiveenclr = x["enclrmul"] * x["lr"] # if effectiveenclr < 0.000005: # return False dimperhead = x["hdim"] / x["numheads"] if dimperhead < 20 or dimperhead > 100: return False return True q.run_experiments(run, ranges, path_prefix=p, check_config=check_config, sourcelang=sourcelang, supportlang=supportlang, testlang=testlang, gpu=gpu, smoothing=smoothing, numbeam=numbeam, trainonvalid=trainonvalid, statesimweight=statesimweight, probsimweight=probsimweight, projmode=projmode)