def main():
# No grads required
torch.set_grad_enabled(False)
args = parse_args()
gen_args = {}
exp = Experiment(args.pop('work_dir'), read_only=True)
validate_args(args, exp)
if exp.model_type == 'binmt':
if not args.get('path'):
Exception('--binmt-path argument is needed for BiNMT model.')
gen_args['path'] = args.pop('binmt_path')
weights = args.get('weights')
if weights:
decoder = Decoder.combo_new(exp,
model_paths=args.pop('model_path'),
weights=weights)
else:
decoder = Decoder.new(exp,
gen_args=gen_args,
model_paths=args.pop('model_path', None),
ensemble=args.pop('ensemble', 1))
if args.pop('interactive'):
if weights:
log.warning(
"Interactive shell not reloadable for combo mode. FIXME: TODO:"
)
if args['input'] != sys.stdin or args['output'] != sys.stdout:
log.warning(
'--input and --output args are not applicable in --interactive mode'
)
args.pop('input')
args.pop('output')
while True:
try:
# an hacky way to unload and reload model when user tries to switch models
decoder.decode_interactive(**args)
break # exit loop if there is no request for reload
except ReloadEvent as re:
decoder = Decoder.new(exp,
gen_args=gen_args,
model_paths=re.model_paths)
args = re.state
# go back to loop and redo interactive shell
else:
return decoder.decode_file(args.pop('input'), args.pop('output'),
**args)
def __test_seq2seq_model__():
"""
batch_size = 4
p = '/Users/tg/work/me/rtg/saral/runs/1S-rnn-basic'
exp = Experiment(p)
steps = 3000
check_pt = 100
trainer = SteppedRNNNMTTrainer(exp=exp, lr=0.01, warmup_steps=100)
trainer.train(steps=steps, check_point=check_pt, batch_size=batch_size)
"""
from rtg.dummy import DummyExperiment
from rtg.module.decoder import Decoder
vocab_size = 50
batch_size = 30
exp = DummyExperiment("tmp.work",
config={'model_type': 'seq'
'2seq'},
read_only=True,
vocab_size=vocab_size)
emb_size = 100
model_dim = 100
steps = 3000
check_pt = 100
assert 2 == Batch.bos_val
src = tensor([[4, 5, 6, 7, 8, 9, 10, 11, 12, 13],
[13, 12, 11, 10, 9, 8, 7, 6, 5, 4]])
src_lens = tensor([src.size(1)] * src.size(0))
for reverse in (False, ):
# train two models;
# first, just copy the numbers, i.e. y = x
# second, reverse the numbers y=(V + reserved - x)
log.info(f"====== REVERSE={reverse}; VOCAB={vocab_size}======")
model, args = RNNMT.make_model('DummyA',
'DummyB',
vocab_size,
vocab_size,
attention='dot',
emb_size=emb_size,
hid_size=model_dim,
n_layers=1)
trainer = SteppedRNNMTTrainer(exp=exp,
model=model,
lr=0.01,
warmup_steps=100)
decr = Decoder.new(exp, model)
def check_pt_callback(**args):
res = decr.greedy_decode(src, src_lens, max_len=17)
for score, seq in res:
log.info(f'{score:.4f} :: {seq}')
trainer.train(steps=steps,
check_point=check_pt,
batch_size=batch_size,
check_pt_callback=check_pt_callback)
def __test_model__():
from rtg.dummy import DummyExperiment
vocab_size = 30
args = {
'src_vocab': vocab_size,
'tgt_vocab': vocab_size,
'enc_layers': 0,
'dec_layers': 4,
'hid_size': 64,
'ff_size': 64,
'n_heads': 4,
'activation': 'gelu'
}
if False:
for n, p in model.named_parameters():
print(n, p.shape)
from rtg.module.decoder import Decoder
config = {
'model_type': 'tfmnmt',
'trainer': {
'init_args': {
'chunk_size': 2
}
}
}
exp = DummyExperiment("work.tmp.t2t",
config=config,
read_only=True,
vocab_size=vocab_size)
exp.model_args = args
trainer = TransformerTrainer(exp=exp, warmup_steps=200)
decr = Decoder.new(exp, trainer.model)
assert 2 == Batch.bos_val
src = tensor(
[[4, 5, 6, 7, 8, 9, 10, 11, 12, 13, Batch.eos_val, Batch.pad_value],
[
13, 12, 11, 10, 9, 8, 7, 6, Batch.eos_val, Batch.pad_value,
Batch.pad_value, Batch.pad_value
]])
src_lens = tensor([src.size(1)] * src.size(0))
def check_pt_callback(**args):
res = decr.greedy_decode(src, src_lens, max_len=12)
for score, seq in res:
log.info(f'{score:.4f} :: {seq}')
batch_size = 50
steps = 1000
check_point = 50
trainer.train(steps=steps,
check_point=check_point,
batch_size=batch_size,
check_pt_callback=check_pt_callback)
def __test_model__():
from rtg.data.dummy import DummyExperiment
from rtg import Batch, my_tensor as tensor
vocab_size = 24
args = {
'src_vocab': vocab_size,
'tgt_vocab': vocab_size,
'enc_layers': 0,
'dec_layers': 4,
'hid_size': 32,
'eff_dims': [],
'dff_dims': [64, 128, 128, 64],
'enc_depth_probs': [],
'dec_depth_probs': [1.0, 0.75, 0.5, 0.75],
'n_heads': 4,
'activation': 'relu'
}
from rtg.module.decoder import Decoder
config = {
'model_type': 'wvskptfmnmt',
'trainer': {'init_args': {'chunk_size': 2, 'grad_accum': 5}},
'optim': {
'args': {
# "cross_entropy", "smooth_kld", "binary_cross_entropy", "triplet_loss"
'criterion': "smooth_kld",
'lr': 0.01,
'inv_sqrt': True
}
}
}
exp = DummyExperiment("work.tmp.wvskptfmnmt", config=config, read_only=True,
vocab_size=vocab_size)
exp.model_args = args
trainer = WVSKPTransformerTrainer(exp=exp, warmup_steps=200, **config['optim']['args'])
decr = Decoder.new(exp, trainer.model)
assert 2 == Batch.bos_val
src = tensor([[4, 5, 6, 7, 8, 9, 10, 11, 12, 13, Batch.eos_val, Batch.pad_value],
[13, 12, 11, 10, 9, 8, 7, 6, Batch.eos_val, Batch.pad_value, Batch.pad_value,
Batch.pad_value]])
src_lens = tensor([src.size(1)] * src.size(0))
def check_pt_callback(**args):
res = decr.greedy_decode(src, src_lens, max_len=12)
for score, seq in res:
log.info(f'{score:.4f} :: {seq}')
batch_size = 50
steps = 200
check_point = 10
trainer.train(steps=steps, check_point=check_point, batch_size=batch_size,
check_pt_callback=check_pt_callback)
def make_model(cls,
src_vocab,
tgt_vocab,
enc_layers=6,
dec_layers=6,
hid_size=512,
ff_size=2048,
n_heads=8,
dropout=0.1,
tied_emb='three-way',
activation='relu',
exp: Experiment = None):
"Helper: Construct a model from hyper parameters."
# get all args for reconstruction at a later phase
_, _, _, args = inspect.getargvalues(inspect.currentframe())
for exclusion in ['cls', 'exp']:
del args[exclusion] # exclude some args
# In case you are wondering, why I didnt use **kwargs here:
# these args are read from conf file where user can introduce errors, so the parameter
# validation and default value assignment is implicitly done by function call for us :)
assert activation in {'relu', 'elu', 'gelu'}
log.info(f"Make model, Args={args}")
c = copy.deepcopy
attn = MultiHeadedAttention(n_heads, hid_size, dropout=dropout)
ff = PositionwiseFeedForward(hid_size,
ff_size,
dropout,
activation=activation)
if enc_layers == 0:
log.warning("Zero encoder layers!")
encoder = Encoder(EncoderLayer(hid_size, c(attn), c(ff), dropout),
enc_layers)
assert dec_layers > 0
decoder = Decoder(
DecoderLayer(hid_size, c(attn), c(attn), c(ff), dropout),
dec_layers)
src_emb = nn.Sequential(Embeddings(hid_size, src_vocab),
PositionalEncoding(hid_size, dropout))
tgt_emb = nn.Sequential(Embeddings(hid_size, tgt_vocab),
PositionalEncoding(hid_size, dropout))
generator = Generator(hid_size, tgt_vocab)
model = cls(encoder, decoder, src_emb, tgt_emb, generator)
if tied_emb:
model.tie_embeddings(tied_emb)
model.init_params()
return model, args
def main():
# No grads required
torch.set_grad_enabled(False)
args = parse_args()
gen_args = {}
exp = Experiment(args.pop('work_dir'), read_only=True)
assert exp.model_type.endswith('lm'), 'Only for Language models'
decoder = Decoder.new(exp, gen_args=gen_args, model_paths=args.pop('model_path', None),
ensemble=args.pop('ensemble', 1))
log_pp = log_perplexity(decoder, args['test'])
print(f'Log perplexity: {log_pp:g}')
print(f'Perplexity: {math.exp(log_pp):g}')
def main():
# No grads required for decode
torch.set_grad_enabled(False)
cli_args = parse_args()
exp = Experiment(cli_args.pop('exp_dir'), read_only=True)
dec_args = exp.config.get('decoder') or exp.config['tester'].get('decoder', {})
validate_args(cli_args, dec_args, exp)
decoder = Decoder.new(exp, ensemble=dec_args.pop('ensemble', 1))
for inp, out in zip(cli_args['input'], cli_args['output']):
log.info(f"Decode :: {inp} -> {out}")
try:
if cli_args.get('no_buffer'):
return decoder.decode_stream(inp, out, **dec_args)
else:
return decoder.decode_file(inp, out, **dec_args)
except:
log.exception(f"Decode failed for {inp}")
def attach_translate_route(cli_args):
global exp
exp = Experiment(cli_args.pop("exp_dir"), read_only=True)
dec_args = exp.config.get("decoder") or exp.config["tester"].get(
"decoder", {})
decoder = Decoder.new(exp, ensemble=dec_args.pop("ensemble", 1))
dataprep = RtgIO(exp=exp)
@bp.route("/translate", methods=["POST", "GET"])
def translate():
if request.method not in ("POST", "GET"):
return "GET and POST are supported", 400
if request.method == 'GET':
sources = request.args.getlist("source", None)
else:
sources = (request.json or {}).get(
'source', None) or request.form.getlist("source")
if isinstance(sources, str):
sources = [sources]
if not sources:
return "Please submit parameter 'source'", 400
sources = [dataprep.pre_process(sent) for sent in sources]
translations = []
for source in sources:
translated = decoder.decode_sentence(source, **dec_args)[0][1]
translated = dataprep.post_process(translated.split())
translations.append(translated)
res = dict(source=sources, translation=translations)
return jsonify(res)
@bp.route("/conf.yml", methods=["GET"])
def get_conf():
conf_str = exp._config_file.read_text(encoding='utf-8',
errors='ignore')
return render_template('conf.yml.html', conf_str=conf_str)
@bp.route("/about", methods=["GET"])
def about():
def_desc = "Model description is unavailable; please update conf.yml"
return render_template('about.html',
model_desc=exp.config.get(
"description", def_desc))
def __test_model__():
from rtg.dummy import DummyExperiment
from rtg import Batch, my_tensor as tensor
vocab_size = 24
args = {
'src_vocab': vocab_size,
'tgt_vocab': vocab_size,
'enc_layers': 4,
'dec_layers': 3,
'hid_size': 128,
'ff_size': 256,
'dec_rnn_type': 'GRU',
'enc_heads': 4
}
from rtg.module.decoder import Decoder
exp = DummyExperiment("work.tmp.hybridmt",
config={'model_type': 'hybridmt'},
read_only=True,
vocab_size=vocab_size)
exp.model_args = args
trainer = HybridMTTrainer(exp=exp, warmup_steps=200)
decr = Decoder.new(exp, trainer.model)
assert 2 == Batch.bos_val
src = tensor([[4, 5, 6, 7, 8, 9, 10, 11, 12, 13],
[13, 12, 11, 10, 9, 8, 7, 6, 5, 4]])
src_lens = tensor([src.size(1)] * src.size(0))
def check_pt_callback(**args):
res = decr.greedy_decode(src, src_lens, max_len=12)
for score, seq in res:
log.info(f'{score:.4f} :: {seq}')
batch_size = 50
steps = 2000
check_point = 50
trainer.train(steps=steps,
check_point=check_point,
batch_size=batch_size,
check_pt_callback=check_pt_callback)
def log_perplexity(decoder: Decoder, test_data: TextIO):
"""
Computes log perplexity of a language model on a given test data
:param decoder:
:param test_data:
:return:
.. math::
P(w_i | h) <-- probability of word w_i given history h
P(w_1, w_2, ... w_N) <-- probability of observing or generating a word sequence
P(w_1, w_2, ... w_N) = P(w_1) x P(w_2|w_1) x P(w_3 | w_1, w_2) ... <-- chain rule
PP_M <-- Perplexity of a Model M
PP_M(w_1, w_2, ... w_N) <-- PP_M on a sequence w_1, w_2, ... w_N
PP_M(w_1, w_2, ... w_N) = P(w_1, w_2, ... w_N)^{-1/N}
log(PP_M) <-- Log perplexity of a model M
log(PP_M) = -1/N \sum_{i=1}^{i=N} P(w_i | w_1, w_2 .. w_{i-1})
Note: log perplexity is a practical solution to deal with floating point underflow
"""
lines = (line.strip() for line in test_data)
test_seqs = [decoder.out_vocab.encode_as_ids(line, add_bos=True, add_eos=True)
for line in lines]
count = 0
total = 0.0
for seq in tqdm(test_seqs, dynamic_ncols=True):
# batch of 1
# TODO: make this faster using bigger batching
batch = torch.tensor(seq, dtype=torch.long, device=device).view(1, -1)
for step in range(1, len(seq)):
# assumption: BOS and EOS are included
count += 1
history = batch[:, :step]
word_idx = seq[step]
log_prob = decoder.next_word_distr(history)[0, word_idx]
total += log_prob
log_pp = -1/count * total
return log_pp.item()
def __test_binmt_model__():
from rtg.module.decoder import Decoder
vocab_size = 20
exp = Experiment("tmp.work",
config={'model_type': 'binmt'},
read_only=True)
num_epoch = 100
emb_size = 100
model_dim = 100
batch_size = 32
src = tensor([[2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13],
[2, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4]])
src_lens = tensor([src.size(1)] * src.size(0))
for reverse in (False, ):
# train two models;
# first, just copy the numbers, i.e. y = x
# second, reverse the numbers y=(V + reserved - x)
log.info(f"====== REVERSE={reverse}; VOCAB={vocab_size}======")
model, args = BiNMT.make_model('DummyA',
'DummyB',
vocab_size,
vocab_size,
emb_size=emb_size,
hid_size=model_dim,
n_layers=2)
trainer = BiNmtTrainer(exp=exp,
model=model,
lr=0.01,
warmup_steps=500,
step_size=2 * batch_size)
decr = Decoder.new(exp, model, gen_args={'path': 'E1D1'})
assert 2 == Batch.bos_val
def print_res(res):
for score, seq in res:
log.info(f'{score:.4f} :: {seq}')
for epoch in range(num_epoch):
model.train()
train_data = BatchIterable(vocab_size,
batch_size,
50,
seq_len=10,
reverse=reverse,
batch_first=True)
val_data = BatchIterable(vocab_size,
batch_size,
5,
reverse=reverse,
batch_first=True)
train_loss = trainer._run_cycle(train_data,
train_data,
train_mode=True)
val_loss = trainer._run_cycle(val_data, val_data, train_mode=False)
log.info(
f"Epoch {epoch}, training Loss: {train_loss:g} \t validation loss:{val_loss:g}"
)
model.eval()
res = decr.greedy_decode(src, src_lens, max_len=17)
print_res(res)
def __init__(self,
exp: Experiment,
model: Optional[NMTModel] = None,
model_factory: Optional[Callable] = None,
optim: str = 'ADAM',
**optim_args):
self.last_step = -1
self.exp = exp
optim_state = None
if model:
self.model = model
else:
args = exp.model_args
assert args
assert model_factory
self.model, args = model_factory(exp=exp, **args)
exp.model_args = args
last_model, self.last_step = self.exp.get_last_saved_model()
if last_model:
log.info(
f"Resuming training from step:{self.last_step}, model={last_model}"
)
state = torch.load(last_model, map_location=device)
model_state = state[
'model_state'] if 'model_state' in state else state
if 'optim_state' in state:
optim_state = state['optim_state']
self.model.load_state_dict(model_state)
if 'amp_state' in state and dtorch.fp16:
log.info("Restoring AMP state")
dtorch._scaler.load_state_dict(state['amp_state'])
else:
log.info(
"No earlier check point found. Looks like this is a fresh start"
)
# optimizer : default args for missing fields
for k, v in self.default_optim_args.items():
optim_args[k] = optim_args.get(k, v)
self.n_gpus = torch.cuda.device_count()
self.device_ids = list(range(self.n_gpus))
inner_opt_args = {
k: optim_args[k]
for k in ['lr', 'betas', 'eps', 'weight_decay', 'amsgrad']
}
self.core_model = self.model.to(device)
trainable_params = self.exp.config['optim'].get('trainable', {})
if trainable_params:
if drtorch.is_distributed: # model is wrapped in DP or DistributedDP
log.warning(
f">> Using more than 1 GPU with 'trainable' params is NOT tested"
)
trainable_params = self.core_model.get_trainable_params(
include=trainable_params.get('include'),
exclude=trainable_params.get('exclude'))
else:
trainable_params = self.model.parameters()
inner_opt = Optims[optim].new(trainable_params, **inner_opt_args)
self.model = dtorch.maybe_distributed(self.core_model)
if optim_state:
log.info("restoring optimizer state from checkpoint")
try:
inner_opt.load_state_dict(optim_state)
except Exception:
log.exception("Unable to restore optimizer, skipping it.")
self.opt = NoamOpt(self.core_model.model_dim,
optim_args['constant'],
optim_args['warmup_steps'],
inner_opt,
step=self.start_step,
inv_sqrt=optim_args['inv_sqrt'])
if self.exp.read_only:
self.tbd = NoOpSummaryWriter()
else:
self.tbd = SummaryWriter(log_dir=str(exp.work_dir / 'tensorboard'))
self.exp.optim_args = optim, optim_args
if not self.exp.read_only:
self.exp.persist_state()
self.samples = None
if exp.samples_file and exp.samples_file.exists():
with IO.reader(exp.samples_file) as f:
self.samples = [line.strip().split('\t') for line in f]
log.info(f"Found {len(self.samples)} sample records")
if self.start_step == 0:
for samp_num, sample in enumerate(self.samples):
self.tbd.add_text(f"sample/{samp_num}",
" || ".join(sample), 0)
from rtg.module.decoder import Decoder
self.decoder = Decoder.new(self.exp, self.core_model)
if self.start_step <= 1:
self.maybe_init_model()
self.criterion = self.create_criterion(optim_args['criterion'])
def tune_decoder_params(self,
exp: Experiment,
tune_src: str,
tune_ref: str,
batch_size: int,
trials: int = 10,
lowercase=True,
beam_size=(1, 4, 8),
ensemble=(1, 5, 10),
lp_alpha=(0.0, 0.4, 0.6),
suggested: List[Tuple[int, int, float]] = None,
**fixed_args):
_, _, _, tune_args = inspect.getargvalues(inspect.currentframe())
tune_args.update(fixed_args)
ex_args = ['exp', 'self', 'fixed_args', 'batch_size', 'max_len']
if trials == 0:
ex_args += ['beam_size', 'ensemble', 'lp_alpha']
for x in ex_args:
del tune_args[x] # exclude some args
_, step = exp.get_last_saved_model()
tune_dir = exp.work_dir / f'tune_step{step}'
log.info(f"Tune dir = {tune_dir}")
tune_dir.mkdir(parents=True, exist_ok=True)
tune_src, tune_ref = Path(tune_src), Path(tune_ref)
assert tune_src.exists()
assert tune_ref.exists()
tune_src, tune_ref = list(IO.get_lines(tune_src)), list(
IO.get_lines(tune_ref))
assert len(tune_src) == len(tune_ref)
tune_log = tune_dir / 'scores.json' # resume the tuning
memory: Dict[Tuple, float] = {}
if tune_log.exists():
data = json.load(tune_log.open())
# JSON keys cant be tuples, so they were stringified
memory = {eval(k): v for k, v in data.items()}
beam_sizes, ensembles, lp_alphas = [], [], []
if suggested:
if isinstance(suggested[0], str):
suggested = [eval(x) for x in suggested]
suggested = [(x[0], x[1], round(x[2], 2)) for x in suggested]
suggested_new = [x for x in suggested if x not in memory]
beam_sizes += [x[0] for x in suggested_new]
ensembles += [x[1] for x in suggested_new]
lp_alphas += [x[2] for x in suggested_new]
new_trials = trials - len(memory)
if new_trials > 0:
beam_sizes += [random.choice(beam_size) for _ in range(new_trials)]
ensembles += [random.choice(ensemble) for _ in range(new_trials)]
lp_alphas += [
round(random.choice(lp_alpha), 2) for _ in range(new_trials)
]
# ensembling is somewhat costlier, so try minimize the model ensembling, by grouping them together
grouped_ens = defaultdict(list)
for b, ens, l in zip(beam_sizes, ensembles, lp_alphas):
grouped_ens[ens].append((b, l))
try:
for ens, args in grouped_ens.items():
decoder = Decoder.new(exp, ensemble=ens)
for b_s, lp_a in args:
eff_batch_size = batch_size // b_s # effective batch size
name = f'tune_step{step}_beam{b_s}_ens{ens}_lp{lp_a:.2f}'
log.info(name)
out_file = tune_dir / f'{name}.out.tsv'
score = self.decode_eval_file(decoder,
tune_src,
out_file,
tune_ref,
batch_size=eff_batch_size,
beam_size=b_s,
lp_alpha=lp_a,
lowercase=lowercase,
**fixed_args)
memory[(b_s, ens, lp_a)] = score
best_params = sorted(memory.items(),
key=lambda x: x[1],
reverse=True)[0][0]
return dict(zip(['beam_size', 'ensemble', 'lp_alpha'],
best_params)), tune_args
finally:
# JSON keys cant be tuples, so we stringify them
data = {str(k): v for k, v in memory.items()}
IO.write_lines(tune_log, json.dumps(data))
def __init__(self,
exp: Experiment,
model: Optional[NMTModel] = None,
model_factory: Optional[Callable] = None,
optim: str = 'ADAM',
**optim_args):
self.start_step = 0
self.last_step = -1
self.exp = exp
optim_state = None
if model:
self.model = model
else:
args = exp.model_args
assert args
assert model_factory
self.model, args = model_factory(exp=exp, **args)
exp.model_args = args
last_model, self.last_step = self.exp.get_last_saved_model()
if last_model:
self.start_step = self.last_step + 1
log.info(
f"Resuming training from step:{self.start_step}, model={last_model}"
)
state = torch.load(last_model)
model_state = state[
'model_state'] if 'model_state' in state else state
if 'optim_state' in state:
optim_state = state['optim_state']
self.model.load_state_dict(model_state)
else:
log.info(
"No earlier check point found. Looks like this is a fresh start"
)
# making optimizer
optim_args['lr'] = optim_args.get('lr', 0.1)
optim_args['betas'] = optim_args.get('betas', [0.9, 0.98])
optim_args['eps'] = optim_args.get('eps', 1e-9)
warmup_steps = optim_args.pop('warmup_steps', 8000)
self._smoothing = optim_args.pop('label_smoothing', 0.1)
constant = optim_args.pop('constant', 2)
self.model = self.model.to(device)
inner_opt = Optims[optim].new(self.model.parameters(), **optim_args)
if optim_state:
log.info("restoring optimizer state from checkpoint")
try:
inner_opt.load_state_dict(optim_state)
except Exception:
log.exception("Unable to restore optimizer, skipping it.")
self.opt = NoamOpt(self.model.model_dim,
constant,
warmup_steps,
inner_opt,
step=self.start_step)
optim_args.update(
dict(warmup_steps=warmup_steps,
label_smoothing=self._smoothing,
constant=constant))
if self.exp.read_only:
self.tbd = NoOpSummaryWriter()
else:
self.tbd = SummaryWriter(log_dir=str(exp.work_dir / 'tensorboard'))
self.exp.optim_args = optim, optim_args
if not self.exp.read_only:
self.exp.persist_state()
self.samples = None
if exp.samples_file.exists():
with IO.reader(exp.samples_file) as f:
self.samples = [line.strip().split('\t') for line in f]
log.info(f"Found {len(self.samples)} sample records")
if self.start_step == 0:
for samp_num, sample in enumerate(self.samples):
self.tbd.add_text(f"sample/{samp_num}",
" || ".join(sample), 0)
from rtg.module.decoder import Decoder
self.decoder = Decoder.new(self.exp, self.model)
if self.start_step == 0:
self.init_embeddings()
self.model = self.model.to(device)
def run_tests(self, exp=None, args=None):
exp = exp or self.exp
args = args or exp.config['tester']
suit: Dict[str, List] = args['suit']
assert suit
log.info(f"Found {len(suit)} suit :: {suit.keys()}")
_, step = exp.get_last_saved_model()
if 'decoder' not in args:
args['decoder'] = {}
dec_args: Dict = args['decoder']
best_params = copy.deepcopy(dec_args)
max_len = best_params.get('max_len', 50)
batch_size = best_params.get('batch_size', 20_000)
# TODO: this has grown to become messy (trying to make backward compatible, improve the logic here
if 'tune' in dec_args and not dec_args['tune'].get('tuned'):
tune_args: Dict = dec_args['tune']
prep_args = exp.config['prep']
if 'tune_src' not in tune_args:
tune_args['tune_src'] = prep_args['valid_src']
if 'tune_ref' not in tune_args:
tune_args['tune_ref'] = prep_args.get('valid_ref',
prep_args['valid_tgt'])
best_params, tuner_args_ext = self.tune_decoder_params(
exp=exp, max_len=max_len, batch_size=batch_size, **tune_args)
log.info(f"tuner args = {tuner_args_ext}")
log.info(f"Tuning complete: best_params: {best_params}")
dec_args['tune'].update(
tuner_args_ext) # Update the config file with default args
dec_args['tune']['tuned'] = True
if 'tune' in best_params:
del best_params['tune']
log.info(f"params: {best_params}")
beam_size = best_params.get('beam_size', 4)
ensemble: int = best_params.pop('ensemble', 5)
lp_alpha = best_params.get('lp_alpha', 0.0)
eff_batch_size = batch_size // beam_size
dec_args.update(
dict(beam_size=beam_size,
lp_alpha=lp_alpha,
ensemble=ensemble,
max_len=max_len,
batch_size=batch_size))
exp.persist_state() # update the config
assert step > 0, 'looks like no model is saved or invalid experiment dir'
test_dir = exp.work_dir / f'test_step{step}_beam{beam_size}_ens{ensemble}_lp{lp_alpha}'
log.info(f"Test Dir = {test_dir}")
test_dir.mkdir(parents=True, exist_ok=True)
decoder = Decoder.new(exp, ensemble=ensemble)
for name, data in suit.items():
# noinspection PyBroadException
src, ref = data, None
out_file = None
if isinstance(data, list):
src, ref = data[:2]
elif isinstance(data, dict):
src, ref = data['src'], data.get('ref')
out_file = data.get('out')
try:
orig_src = Path(src).resolve()
src_link = test_dir / f'{name}.src'
ref_link = test_dir / f'{name}.ref'
buffer = [(src_link, orig_src)]
if ref:
orig_ref = Path(ref).resolve()
buffer.append((ref_link, orig_ref))
for link, orig in buffer:
if not link.exists():
link.symlink_to(orig)
out_file = test_dir / f'{name}.out.tsv' if not out_file else out_file
out_file.parent.mkdir(parents=True, exist_ok=True)
self.decode_eval_file(decoder,
src_link,
out_file,
ref_link,
batch_size=eff_batch_size,
beam_size=beam_size,
lp_alpha=lp_alpha,
max_len=max_len)
except Exception as e:
log.exception(f"Something went wrong with '{name}' test")
err = test_dir / f'{name}.err'
err.write_text(str(e))
def export(self,
target: Path,
name: str = None,
ensemble: int = 1,
copy_config=True,
copy_vocab=True):
to_exp = Experiment(target.resolve(), config=self.exp.config)
if copy_config:
log.info("Copying config")
to_exp.persist_state()
if copy_vocab:
log.info("Copying vocabulary")
self.exp.copy_vocabs(to_exp)
assert ensemble > 0
assert name
assert len(name.split()) == 1
log.info("Going to average models and then copy")
model_paths = self.exp.list_models()[:ensemble]
log.info(f'Model paths: {model_paths}')
chkpt_state = torch.load(model_paths[0], map_location=device)
if ensemble > 1:
log.info("Averaging them ...")
avg_state = Decoder.average_states(model_paths)
chkpt_state = dict(model_state=avg_state,
model_type=chkpt_state['model_type'],
model_args=chkpt_state['model_args'])
log.info("Instantiating it ...")
model = instantiate_model(checkpt_state=chkpt_state, exp=self.exp)
log.info(f"Exporting to {target}")
to_exp = Experiment(target, config=self.exp.config)
to_exp.persist_state()
IO.copy_file(self.exp.model_dir / 'scores.tsv',
to_exp.model_dir / 'scores.tsv')
if (self.exp.work_dir / 'rtg.zip').exists():
IO.copy_file(self.exp.work_dir / 'rtg.zip',
to_exp.work_dir / 'rtg.zip')
src_chkpt = chkpt_state
log.warning(
"step number, training loss and validation loss are not recalculated."
)
step_num, train_loss, val_loss = [
src_chkpt.get(n, -1) for n in ['step', 'train_loss', 'val_loss']
]
copy_fields = [
'optim_state', 'step', 'train_loss', 'valid_loss', 'time',
'rtg_version', 'model_type', 'model_args'
]
state = dict((c, src_chkpt[c]) for c in copy_fields if c in src_chkpt)
state['model_state'] = model.state_dict()
state['averaged_time'] = time.time()
state['model_paths'] = model_paths
state['num_checkpts'] = len(model_paths)
prefix = f'model_{name}_avg{len(model_paths)}'
to_exp.store_model(step_num,
state,
train_score=train_loss,
val_score=val_loss,
keep=10,
prefix=prefix)
chkpts = [mp.name for mp in model_paths]
status = {
'parent': str(self.exp.work_dir),
'ensemble': ensemble,
'checkpts': chkpts,
'when': datetime.datetime.now().isoformat(),
'who': os.environ.get('USER', '<unknown>'),
}
yaml.dump(status, stream=to_exp.work_dir / '_EXPORTED')
if self.exp._trained_flag.exists():
IO.copy_file(self.exp._trained_flag, to_exp._trained_flag)
def inherit_parent(self):
parent = self.config['parent']
parent_exp = TranslationExperiment(parent['experiment'],
read_only=True)
log.info(f"Parent experiment: {parent_exp.work_dir}")
parent_exp.has_prepared()
vocab_sepc = parent.get('vocab')
if vocab_sepc:
log.info(f"Parent vocabs inheritance spec: {vocab_sepc}")
codec_lib = parent_exp.config['prep'].get('codec_lib')
if codec_lib:
self.config['prep']['codec_lib'] = codec_lib
def _locate_field_file(exp: TranslationExperiment,
name,
check_exists=False) -> Path:
switch = {
'src': exp._src_field_file,
'tgt': exp._tgt_field_file,
'shared': exp._shared_field_file
}
assert name in switch, f'{name} not allowed; valid options= {switch.keys()}'
file = switch[name]
if check_exists:
assert file.exists(
), f'{file} doesnot exist; for {name} of {exp.work_dir}'
return file
for to_field, from_field in vocab_sepc.items():
from_field_file = _locate_field_file(parent_exp,
from_field,
check_exists=True)
to_field_file = _locate_field_file(self,
to_field,
check_exists=False)
IO.copy_file(from_field_file, to_field_file)
self.reload_vocabs()
else:
log.info("No vocabularies are inherited from parent")
model_sepc = parent.get('model')
if model_sepc:
log.info("Parent model inheritance spec")
if model_sepc.get('args'):
self.model_args = parent_exp.model_args
ensemble = model_sepc.get('ensemble', 1)
model_paths = parent_exp.list_models(sort='step',
desc=True)[:ensemble]
log.info(
f"Averaging {len(model_paths)} checkpoints of parent model: \n{model_paths}"
)
from rtg.module.decoder import Decoder
avg_state = Decoder.average_states(model_paths=model_paths)
log.info(
f"Saving parent model's state to {self.parent_model_state}")
torch.save(avg_state, self.parent_model_state)
shrink_spec = parent.get('shrink')
if shrink_spec:
remap_src, remap_tgt = self.shrink_vocabs()
def map_rows(mapping: List[int], source: torch.Tensor, name=''):
assert max(mapping) < len(source)
target = torch.zeros((len(mapping), *source.shape[1:]),
dtype=source.dtype,
device=source.device)
for new_idx, old_idx in enumerate(mapping):
target[new_idx] = source[old_idx]
log.info(f"Mapped {name} {source.shape} --> {target.shape} ")
return target
""" src_embed.0.lut.weight [N x d]
tgt_embed.0.lut.weight [N x d]
generator.proj.weight [N x d]
generator.proj.bias [N] """
if remap_src:
key = 'src_embed.0.lut.weight'
avg_state[key] = map_rows(remap_src, avg_state[key], name=key)
if remap_tgt:
map_keys = [
'tgt_embed.0.lut.weight', 'generator.proj.weight',
'generator.proj.bias'
]
for key in map_keys:
if key not in avg_state:
log.warning(
f'{key} not found in avg_state of parent model. Mapping skipped'
)
continue
avg_state[key] = map_rows(remap_tgt,
avg_state[key],
name=key)
if self.parent_model_state.exists():
self.parent_model_state.rename(
self.parent_model_state.with_suffix('.orig'))
torch.save(avg_state, self.parent_model_state)
self.persist_state(
) # this will fix src_vocab and tgt_vocab of model_args conf
def export(self,
target: Path,
name: str = None,
ensemble: int = 1,
copy_config=True,
copy_vocab=True):
to_exp = Experiment(target, config=self.exp.config)
if copy_config:
log.info("Copying config")
to_exp.persist_state()
if copy_vocab:
log.info("Copying vocabulary")
self.exp.copy_vocabs(to_exp)
if ensemble > 0:
assert name
assert len(name.split()) == 1
log.info("Going to average models and then copy")
model_paths = self.exp.list_models()[:ensemble]
log.info(f'Model paths: {model_paths}')
checkpts = [torch.load(mp) for mp in model_paths]
states = [chkpt['model_state'] for chkpt in checkpts]
log.info("Averaging them ...")
avg_state = Decoder.average_states(*states)
chkpt_state = dict(model_state=avg_state,
model_type=checkpts[0]['model_type'],
model_args=checkpts[0]['model_args'])
log.info("Instantiating it ...")
model = instantiate_model(checkpt_state=chkpt_state, exp=self.exp)
log.info(f"Exporting to {target}")
to_exp = Experiment(target, config=self.exp.config)
to_exp.persist_state()
src_chkpt = checkpts[0]
log.warning(
"step number, training loss and validation loss are not recalculated."
)
step_num, train_loss, val_loss = [
src_chkpt.get(n, -1)
for n in ['step', 'train_loss', 'val_loss']
]
copy_fields = [
'optim_state', 'step', 'train_loss', 'valid_loss', 'time',
'rtg_version', 'model_type', 'model_args'
]
state = dict(
(c, src_chkpt[c]) for c in copy_fields if c in src_chkpt)
state['model_state'] = model.state_dict()
state['averaged_time'] = time.time()
state['model_paths'] = model_paths
state['num_checkpts'] = len(model_paths)
prefix = f'model_{name}_avg{len(model_paths)}'
to_exp.store_model(step_num,
state,
train_score=train_loss,
val_score=val_loss,
keep=10,
prefix=prefix)