def convert_bart_checkpoint(checkpoint_path, pytorch_dump_folder_path):
"""
Copy/paste/tweak model's weights to our BERT structure.
"""
bart = torch.hub.load("pytorch/fairseq", checkpoint_path)
bart.eval() # disable dropout
bart.model.upgrade_state_dict(bart.model.state_dict())
hf_model_name = checkpoint_path.replace(".", "-")
config = BartConfig.from_pretrained(hf_model_name)
tokens = bart.encode(SAMPLE_TEXT).unsqueeze(0)
tokens2 = BartTokenizer.from_pretrained(hf_model_name).encode(
SAMPLE_TEXT, return_tensors="pt").unsqueeze(0)
assert torch.eq(tokens, tokens2).all()
if checkpoint_path in ["bart.large", "bart.large.cnn"]:
state_dict = bart.model.state_dict()
for k in IGNORE_KEYS:
state_dict.pop(k, None)
state_dict["shared.weight"] = state_dict["decoder.embed_tokens.weight"]
model = BartModel(config)
their_output = bart.extract_features(tokens)
else: # MNLI Case
state_dict = bart.state_dict()
for k in IGNORE_KEYS:
state_dict.pop(k, None)
state_dict["model.shared.weight"] = state_dict[
"model.decoder.embed_tokens.weight"]
for src, dest in rename_keys:
rename_key(state_dict, src, dest)
model = BartForSequenceClassification(config)
their_output = bart.eval("mnli", tokens, return_logits=True)
# Load state dict
model.load_state_dict(state_dict)
model.eval()
# Check results
if checkpoint_path == "bart.large.cnn": # generate doesnt work yet
model = BartForMaskedLM(config, base_model=model)
assert "lm_head.weight" in model.state_dict()
assert model.lm_head.out_features == config.max_position_embeddings
model.eval()
our_outputs = model.model.forward(tokens)[0]
else:
our_outputs = model.forward(tokens)[0]
assert their_output.shape == our_outputs.shape
assert (their_output == our_outputs).all().item()
Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
model.save_pretrained(pytorch_dump_folder_path)
def convert_bart_checkpoint(checkpoint_path, pytorch_dump_folder_path):
"""
Copy/paste/tweak model's weights to our BERT structure.
"""
b2 = torch.hub.load("pytorch/fairseq", checkpoint_path)
b2.eval() # disable dropout
b2.model.upgrade_state_dict(b2.model.state_dict())
config = BartConfig()
tokens = b2.encode(SAMPLE_TEXT).unsqueeze(0)
tokens2 = BartTokenizer.from_pretrained("bart-large").encode(
SAMPLE_TEXT).unsqueeze(0)
assert torch.eq(tokens, tokens2).all()
# assert their_output.size() == (1, 11, 1024)
if checkpoint_path == "bart.large":
state_dict = b2.model.state_dict()
state_dict["shared.weight"] = state_dict["decoder.embed_tokens.weight"]
model = BartModel(config)
their_output = b2.extract_features(tokens)
else: # MNLI Case
state_dict = b2.state_dict()
state_dict["model.shared.weight"] = state_dict[
"model.decoder.embed_tokens.weight"]
for src, dest in rename_keys:
rename_key(state_dict, src, dest)
state_dict.pop("_float_tensor", None)
model = BartForSequenceClassification(config)
their_output = b2.predict("mnli", tokens, return_logits=True)
for k in IGNORE_KEYS:
state_dict.pop(k, None)
model.load_state_dict(state_dict)
model.eval()
our_outputs = model.forward(tokens)[0]
assert their_output.shape == our_outputs.shape
assert (their_output == our_outputs).all().item()
Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
model.save_pretrained(pytorch_dump_folder_path)
