flax_to_pytorch.py

from transformers import T5ForConditionalGeneration, TFT5ForConditionalGeneration

pt_model = T5ForConditionalGeneration.from_pretrained(".", from_flax=True)
pt_model.save_pretrained(".")

# tf_model = TFT5ForConditionalGeneration.from_pretrained(".", from_pt=True)
# tf_model.save_pretrained(".")


exit()



# from transformers import T5ForConditionalGeneration, TFT5ForConditionalGeneration, FlaxT5ForConditionalGeneration
# import numpy as np
# import torch
#
# fx_model = FlaxT5ForConditionalGeneration.from_pretrained(".")
#
# pt_model = T5ForConditionalGeneration.from_pretrained(".", from_flax=True)
# pt_model.save_pretrained(".")
#
#
# # tf_model = TFT5ForConditionalGeneration.from_pretrained(".", from_pt=True)
# # tf_model.save_pretrained(".")
#

#!/usr/bin/env python
import tempfile
import jax
import numpy as np
import torch
from jax import numpy as jnp
from transformers import AutoTokenizer, FlaxT5ForConditionalGeneration, T5ForConditionalGeneration

def to_f32(t):
    return jax.tree_map(lambda x: x.astype(jnp.float32) if x.dtype == jnp.bfloat16 else x, t)

def main():
    # Saving extra files from config.json and tokenizer.json files
    tokenizer = AutoTokenizer.from_pretrained("./")
    tokenizer.save_pretrained("./")
    # Temporary saving bfloat16 Flax model into float32
    tmp = tempfile.mkdtemp()
    flax_model = FlaxT5ForConditionalGeneration.from_pretrained("./")
    flax_model.params = to_f32(flax_model.params)
    flax_model.save_pretrained(tmp)
    # Converting float32 Flax to PyTorch
    pt_model = T5ForConditionalGeneration.from_pretrained(tmp, from_flax=True)
    pt_model.save_pretrained("./", save_config=False)

    input_ids = np.asarray(2 * [128 * [0]], dtype=np.int32)
    input_ids_pt = torch.tensor(input_ids)
    logits_pt = pt_model(input_ids_pt).logits
    print(logits_pt)
    logits_fx = flax_model(input_ids).logits
    print(logits_fx)

if __name__ == "__main__":
    main()