test cosmos

src/testcosmos.py

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+import jax
+print(jax.local_device_count())
+import jax.numpy as jnp
+
+import flax
+import flax.linen as nn
+from flax.core.frozen_dict import FrozenDict, unfreeze
+from flax.training.common_utils import get_metrics,onehot,shard,shard_prng_key
+
+from transformers import  GPTNeoConfig
+from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoPreTrainedModel
+from transformers import GPT2Tokenizer
+
+from datasets import load_dataset
+import pandas as pd
+
+num_choices=4
+dataset = load_dataset("cosmos_qa")
+
+def preprocess(example):
+    example['context&question']=example['context']+example['question']
+    example['first_sentence']=[example['context&question']]*num_choices
+    example['second_sentence']=[example[f'answer{i}'] for i in range(num_choices)]
+    return example
+
+test_dataset=dataset['test'].map(preprocess)
+
+len_test_dataset=100
+
+test_dataset=test_dataset.select(range(len_test_dataset))
+
+tokenizer=GPT2Tokenizer.from_pretrained('EleutherAI/gpt-neo-1.3B',pad_token='<|endoftext|>')
+
+remove_col=test_dataset.column_names
+
+def tokenize(examples):
+    tokenized_examples=tokenizer(examples['first_sentence'],examples['second_sentence'],padding='max_length',truncation=True,max_length=256,return_tensors='jax')
+    return tokenized_examples
+
+test_dataset=test_dataset.map(tokenize)
+
+test_dataset=test_dataset.remove_columns(remove_col)
+list1=[]
+
+def glue_test_data_loader(rng,dataset,batch_size):
+  steps_per_epoch=len_test_dataset//batch_size
+  perms=jax.random.permutation(rng,len_test_dataset)
+  perms=perms[:steps_per_epoch*batch_size]
+  perms=perms.reshape((steps_per_epoch,batch_size))
+  for perm in perms:
+    list1.append(perm)
+    batch=dataset[perm]
+    #print(jnp.array(batch['label']))
+    batch={k:jnp.array(v) for k,v in batch.items()}
+    #batch=shard(batch)
+    yield batch
+
+seed=0
+rng=jax.random.PRNGKey(seed)
+dropout_rngs=jax.random.split(rng,jax.local_device_count())
+
+input_id=jnp.array(test_dataset['input_ids'])
+att_mask=jnp.array(test_dataset['attention_mask'])
+
+total_batch_size=16
+
+from  model_file  import FlaxGPTNeoForMultipleChoice
+
+model = FlaxGPTNeoForMultipleChoice.from_pretrained('Vivek/gptneo_hellaswag',input_shape=(1,num_choices,1))
+
+restored_output=[]
+rng, input_rng = jax.random.split(rng)
+for idx,batch in enumerate(glue_test_data_loader(input_rng, test_dataset, total_batch_size)):
+    outputs=model(batch['input_ids'],batch['attention_mask'])
+    final_output=jnp.argmax(outputs,axis=-1)
+    restored_output.append(final_output)
+
+finall=pd.DataFrame({'predictions':restored_output,'permutation':list1})
+finall.to_csv('./cosmos_predictions.csv')