def handle(srcf, rsf, h5args=h5zipargs):
if srcf == rsf:
h5save(h5load(srcf, restore_list=False), rsf, h5args=h5args)
else:
with h5File(srcf, "r") as sfg, h5File(rsf, 'w') as rfg:
handle_group(sfg, rfg, h5args=h5args)
def handle(finput, ftarget, fvocab_i, fvocab_t, frs, minbsize=1, expand_for_mulgpu=True, bsize=max_sentences_gpu, maxpad=max_pad_tokens_sentence, maxpart=normal_tokens_vs_pad_tokens, maxtoken=max_tokens_gpu, minfreq=False, vsize=False):
vcbi, nwordi = ldvocab(fvocab_i, minf=minfreq, omit_vsize=vsize, vanilla=False)
vcbt, nwordt = ldvocab(fvocab_t, minf=minfreq, omit_vsize=vsize, vanilla=False)
if expand_for_mulgpu:
_bsize = bsize * minbsize
_maxtoken = maxtoken * minbsize
else:
_bsize = bsize
_maxtoken = maxtoken
with h5File(frs,'w') as rsf:
src_grp = rsf.create_group("src")
tgt_grp = rsf.create_group("tgt")
curd = 0
for i_d, td in batch_padder(finput, ftarget, vcbi, vcbt, _bsize, maxpad, maxpart, _maxtoken, minbsize):
rid = numpy.array(i_d, dtype=numpy.int32)
rtd = numpy.array(td, dtype=numpy.int32)
#rld = numpy.array(ld, dtype=numpy.int32)
wid = str(curd)
src_grp.create_dataset(wid, data=rid, **h5datawargs)
tgt_grp.create_dataset(wid, data=rtd, **h5datawargs)
#rsf["l" + wid] = rld
curd += 1
rsf["ndata"] = numpy.array([curd], dtype=numpy.int32)
rsf["nword"] = numpy.array([nwordi, nwordt], dtype=numpy.int32)
print("Number of batches: %d\nSource Vocabulary Size: %d\nTarget Vocabulary Size: %d" % (curd, nwordi, nwordt,))
def handle(cnfg, srcmtf, decf, rsf):
with h5File(cnfg.dev_data, "r") as tdf:
nwordi, nwordt = tdf["nword"][:].tolist()
mymodel = NMT(cnfg.isize, nwordi, nwordt, cnfg.nlayer, cnfg.ff_hsize,
cnfg.drop, cnfg.attn_drop, cnfg.share_emb, cnfg.nhead,
cache_len_default, cnfg.attn_hsize, cnfg.norm_output,
cnfg.bindDecoderEmb, cnfg.forbidden_indexes,
cnfg.num_layer_fwd)
init_model_params(mymodel)
_tmpm = NMTBase(cnfg.isize, nwordi, nwordt, cnfg.nlayer, cnfg.ff_hsize,
cnfg.drop, cnfg.attn_drop, cnfg.share_emb, cnfg.nhead,
cache_len_default, cnfg.attn_hsize, cnfg.norm_output,
cnfg.bindDecoderEmb, cnfg.forbidden_indexes)
_tmpm = init_model_params(_tmpm)
_tmpm = load_model_cpu(srcmtf, _tmpm)
mymodel.load_base(_tmpm)
mymodel.dec = load_model_cpu(decf, mymodel.dec)
if cnfg.share_emb:
mymodel.dec.wemb.weight = _tmpm.enc.wemb.weight
if cnfg.bindDecoderEmb:
mymodel.dec.classifier.weight = mymodel.dec.wemb.weight
_tmpm = None
save_model(mymodel, rsf, sub_module=False, h5args=h5zipargs)
def handle(finput, ftarget, fvocab_i, fvocab_t, frs, minbsize=1, expand_for_mulgpu=True, bsize=max_sentences_gpu, maxpad=max_pad_tokens_sentence, maxpart=normal_tokens_vs_pad_tokens, maxtoken=max_tokens_gpu, minfreq=False, vsize=False):
vcbi, nwordi = ldvocab(fvocab_i, minf=minfreq, omit_vsize=vsize, vanilla=False)
vcbt, nwordt = ldvocab(fvocab_t, minf=minfreq, omit_vsize=vsize, vanilla=False)
if expand_for_mulgpu:
_bsize = bsize * minbsize
_maxtoken = maxtoken * minbsize
else:
_bsize = bsize
_maxtoken = maxtoken
with h5File(frs, 'w') as rsf:
src_grp = rsf.create_group("src")
tgt_grp = rsf.create_group("tgt")
curd = {}
for i_d, td, nsent in batch_padder(finput, ftarget, vcbi, vcbt, _bsize, maxpad, maxpart, _maxtoken, minbsize):
rid = numpy.array(i_d, dtype=numpy.int32)
rtd = numpy.array(td, dtype=numpy.int32)
_nsentgid = str(nsent)
_curd = curd.get(nsent, 0)
if _curd == 0:
src_grp.create_group(_nsentgid)
tgt_grp.create_group(_nsentgid)
_curid = str(_curd)
src_grp[_nsentgid].create_dataset(_curid, data=rid, **h5datawargs)
tgt_grp[_nsentgid].create_dataset(_curid, data=rtd, **h5datawargs)
curd[nsent] = _curd + 1
sents, ndl = dict2pairs(curd)
rsf["nsent"] = numpy.array(sents, dtype=numpy.int32)
rsf["ndata"] = numpy.array(ndl, dtype=numpy.int32)
rsf["nword"] = numpy.array([nwordi, nwordt], dtype=numpy.int32)
print("Number of batches: %d\nSource Vocabulary Size: %d\nTarget Vocabulary Size: %d" % (sum(ndl), nwordi, nwordt,))
def handle(finput,
fvocab_i,
fvocab_task,
frs,
minbsize=1,
expand_for_mulgpu=True,
bsize=max_sentences_gpu,
maxpad=max_pad_tokens_sentence,
maxpart=normal_tokens_vs_pad_tokens,
maxtoken=max_tokens_gpu,
minfreq=False,
vsize=False):
vcbi, nwordi = ldvocab(fvocab_i,
minf=minfreq,
omit_vsize=vsize,
vanilla=False)
vcbtask, nwordtask = ldvocab(fvocab_task,
minf=False,
omit_vsize=False,
vanilla=True)
if expand_for_mulgpu:
_bsize = bsize * minbsize
_maxtoken = maxtoken * minbsize
else:
_bsize = bsize
_maxtoken = maxtoken
with h5File(frs, 'w') as rsf:
curd = {}
torder = []
for i_d, taskd in batch_padder(finput, vcbi, vcbtask, _bsize, maxpad,
maxpart, _maxtoken, minbsize):
_str_taskd = str(taskd)
if _str_taskd in rsf:
src_grp = rsf[_str_taskd]["src"]
else:
src_grp = rsf.create_group(_str_taskd).create_group("src")
torder.append(taskd)
rid = numpy.array(i_d, dtype=numpy.int32)
_id = curd.get(taskd, 0)
wid = str(_id)
src_grp.create_dataset(wid, data=rid, **h5datawargs)
curd[taskd] = _id + 1
rsf["taskorder"] = numpy.array(torder, dtype=numpy.int32)
curd = [curd[tmp] for tmp in torder]
rsf["ndata"] = numpy.array(curd, dtype=numpy.int32)
rsf["nword"] = numpy.array([nwordi, nwordtask], dtype=numpy.int32)
print(
"Number of batches: %d\nSource Vocabulary Size: %d\nNumber of Tasks: %d"
% (
sum(curd),
nwordi,
nwordtask,
))
def handle(h5f, bsize, shuf=True):
ntoken = 0
rsl = []
with h5File(h5f, "r") as td:
ntest = td["ndata"][:].item()
tl = list(range(ntest))
if shuf:
shuffle(tl)
tgt_grp = td["tgt"]
for tid in tqdm(tl, mininterval=tqdm_mininterval):
seq_batch = torch.from_numpy(tgt_grp[str(tid)][:])
ot = seq_batch.narrow(-1, 1, seq_batch.size(-1) - 1)
ntoken += ot.ne(0).int().sum().item()
if ntoken >= bsize:
rsl.append(ntoken)
ntoken = 0
return sum(rsl) / float(len(rsl))
def handle(h5f, bsize, shuf=True):
with h5File(h5f, "r") as td:
tl = [(
str(nsent),
str(_curd),
) for nsent, ndata in zip(td["nsent"][:].tolist(), td["ndata"]
[:].tolist()) for _curd in range(ndata)]
if shuf:
shuffle(tl)
tgt_grp = td["tgt"]
ntoken = 0
nstep = 0
for nsent, i_d in tqdm(tl, mininterval=tqdm_mininterval):
seq_batch = torch.from_numpy(tgt_grp[nsent][i_d][:])
ot = seq_batch.narrow(-1, 1, seq_batch.size(-1) - 1)
ntoken += ot.ne(0).int().sum().item()
if ntoken >= bsize:
nstep += 1
ntoken = 0
return nstep
def handle(h5f, bsize, shuf=True):
with h5File(h5f, "r") as td:
ntest = td["ndata"][:].tolist()
tl = [(
i,
str(_task),
) for _nd, _task in zip(ntest, td["taskorder"][:].tolist())
for i in range(_nd)]
if shuf:
shuffle(tl)
ntoken = 0
nstep = 0
for tid, taskid in tqdm(tl, mininterval=tqdm_mininterval):
seq_batch = torch.from_numpy(td[taskid]["tgt"][str(tid)][:])
ot = seq_batch.narrow(-1, 1, seq_batch.size(-1) - 1)
ntoken += ot.ne(0).int().sum().item()
if ntoken >= bsize:
nstep += 1
ntoken = 0
return nstep
def handle(srcf):
with h5File(srcf, "r") as sfg:
rs = handle_group(sfg)
print(rs)