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Update convert_to_hf_gptneox.py #108

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Update convert_to_hf_gptneox.py #108

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27 changes: 14 additions & 13 deletions tools/convert_to_hf_gptneox.py
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Original file line number Diff line number Diff line change
Expand Up @@ -54,18 +54,20 @@ def load_decentralized_checkpoint(model, checkpoint_path, n_stages=2, n_layer_pe
# torch.save(_tmp, os.path.join(output_path, f'pytorch_{j}.pt'))
model.gpt_neox.layers[j].load_state_dict(_tmp)

elif i == n_stages - 1:
for j in range(n_layer_per_stage):
if i != 0 and i == n_stages - 1 or n_stages == 1:
if n_stages != 1:
for j in range(n_layer_per_stage):
_tmp = {k[len(f"{j}."):]:v for k,v in checkpoint.items() if k.startswith(f"{j}.")}
if len(_tmp) == 0:
break
# torch.save(_tmp, os.path.join(output_path, f'pytorch_{i*n_layer_per_stage + j}.pt'))
model.gpt_neox.layers[i*n_layer_per_stage + j].load_state_dict(_tmp)
if i*n_layer_per_stage + j == len(model.gpt_neox.layers) - 1:
j += 1
break
_tmp = {k[len(f"{j}."):]:v for k,v in checkpoint.items() if k.startswith(f"{j}.")}
if len(_tmp) == 0:
break
# torch.save(_tmp, os.path.join(output_path, f'pytorch_{i*n_layer_per_stage + j}.pt'))
model.gpt_neox.layers[i*n_layer_per_stage + j].load_state_dict(_tmp)
if i*n_layer_per_stage + j == len(model.gpt_neox.layers) - 1:
j += 1
break

_tmp = {k[len(f"{j}."):]:v for k,v in checkpoint.items() if k.startswith(f"{j}.")}
else:
_tmp = {k[len(f"{n_layer_per_stage+1}."):]:v for k,v in checkpoint.items() if k.startswith(f"{n_layer_per_stage+1}.")} #Added line
if len(_tmp) == 0:
break
# torch.save(_tmp, os.path.join(output_path, f'pytorch_lm_head.pt'))
Expand All @@ -75,7 +77,7 @@ def load_decentralized_checkpoint(model, checkpoint_path, n_stages=2, n_layer_pe
if 'embed_out.bias' in _tmp:
model.embed_out.bias.data[:] = _tmp['embed_out.bias']

else:
elif i != 0:
for j in range(n_layer_per_stage):
_tmp = {k[len(f"{j}."):]:v for k,v in checkpoint.items() if k.startswith(f"{j}.")}
if len(_tmp) == 0:
Expand Down Expand Up @@ -130,4 +132,3 @@ def load_decentralized_checkpoint(model, checkpoint_path, n_stages=2, n_layer_pe
print(f'saved HF model to `{args.save_path}`')
config.save_pretrained(args.save_path)
tokenizer.save_pretrained(args.save_path)