| import torch | |
| import numpy as np | |
| import jax | |
| import jax.numpy as jnp | |
| from transformers import AutoTokenizer | |
| from transformers import FlaxGPTNeoForCausalLM | |
| from transformers import GPTNeoForCausalLM | |
| tokenizer = AutoTokenizer.from_pretrained(".") | |
| tokenizer.pad_token = tokenizer.eos_token | |
| model_fx = FlaxGPTNeoForCausalLM.from_pretrained(".") | |
| # def to_f32(t): | |
| # return jax.tree_map(lambda x: x.astype(jnp.float32) if x.dtype == jnp.bfloat16 else x, t) | |
| # model_fx.params = to_f32(model_fx.params) | |
| # model_fx.save_pretrained("./fx") | |
| model_pt = GPTNeoForCausalLM.from_pretrained(".", from_flax=True) | |
| model_pt.save_pretrained(".") | |
| input_ids = np.asarray(2 * [128 * [0]], dtype=np.int32) | |
| input_ids_pt = torch.tensor(input_ids) | |
| logits_pt = model_pt(input_ids_pt).logits | |
| print(logits_pt) | |
| logits_fx = model_fx(input_ids).logits | |
| print(logits_fx) |