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)
