def load(model_path: str):
dict_path = model_path + ".dict.pt"
model = NMTModel()
print("Loading whole model")
load_partial_state_dict(model, torch.load(dict_path))
return model
def load(model_path: str):
dict_path = model_path+".dict.pt"
model = PrunedModel()
print("Loading whole model")
load_partial_state_dict(model, torch.load(dict_path))
if ".pruned" in model_path:
model.currently_pruned = True
else:
model.currently_pruned = False
return model
def load(model_path: str):
enc_path = model_path+".enc.pt"
dec_path = model_path+".dec.pt"
model = NMTModel()
print("Loading encoder")
load_partial_state_dict(model.encoder, torch.load(enc_path))
print("Loading decoder")
load_partial_state_dict(model.decoder, torch.load(dec_path))
return model
def load(model_path: str):
dict_path = model_path+".dict.pt"
model = MixedPrecisionModel()
print("Loading whole model")
if ".quantized" in model_path:
model.quantize()
else:
model.unquantize()
load_partial_state_dict(model, torch.load(dict_path))
if ".pruned" in model_path:
model.currently_pruned = True
else:
model.currently_pruned = False
return model
def load(model_path: str):
dict_path = model_path+".dict.pt"
print("Loading whole model")
if ".postfactorized" in model_path:
print("loading factorized model")
tconfig.embedding_factorization = True
tconfig.ffward_factorization = True
tconfig.inner_factorization = True
tconfig.embedding_rank = 256
tconfig.ffward_rank = 256
tconfig.inner_rank = 256
model = PostFactorizedModel(embedding_rank=256, ffward_rank=256, inner_rank=256)
model.currently_factorized = ["embeddings", "ffward", "attention"]
else:
print("loading standard model")
model = PostFactorizedModel()
load_partial_state_dict(model, torch.load(dict_path))
return model
def load(model_path: str):
model = MultiWayModel()
print("Loading decoder")
dec_path = model_path + ".dec.pt"
load_partial_state_dict(model.decoder, torch.load(dec_path))
print("Loading encoders")
for key in model.keys:
enc_path = model_path + "." + key + ".enc.pt"
load_partial_state_dict(model.encoder[key], torch.load(enc_path))
if model.use_discriminator:
print("Loading discriminator")
try:
disc_path = model_path + ".disc.pt"
load_partial_state_dict(model.discriminator,
torch.load(disc_path))
except:
print("Failed")
return model
