28 lines
799 B
Python
28 lines
799 B
Python
from transformers import AutoModelForCausalLM, FlaxAutoModelForCausalLM, AutoTokenizer
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import torch
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import numpy as np
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import jax
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import jax.numpy as jnp
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def to_f32(t):
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return jax.tree_map(lambda x: x.astype(jnp.float32) if x.dtype == jnp.bfloat16 else x, t)
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jax.config.update('jax_platform_name', 'cpu')
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MODEL_PATH = "./"
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model = FlaxAutoModelForCausalLM.from_pretrained(MODEL_PATH)
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model.params = to_f32(model.params)
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model.save_pretrained(MODEL_PATH)
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pt_model = AutoModelForCausalLM.from_pretrained(
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MODEL_PATH, from_flax=True).to('cpu')
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input_ids = np.asarray(2 * [128 * [0]], dtype=np.int32)
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input_ids_pt = torch.tensor(input_ids)
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logits_pt = pt_model(input_ids_pt).logits
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print(logits_pt)
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logits_fx = model(input_ids).logits
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print(logits_fx)
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pt_model.save_pretrained(MODEL_PATH)
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