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62 lines
2.0 KiB
Python
62 lines
2.0 KiB
Python
import tensorflow as tf
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from transformers import BertModel, BertTokenizer, TFBertModel
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from amdshark.amdshark_inference import AMDSharkInference
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MAX_SEQUENCE_LENGTH = 512
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BATCH_SIZE = 1
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# Create a set of 2-dimensional inputs
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bert_input = [
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tf.TensorSpec(shape=[BATCH_SIZE, MAX_SEQUENCE_LENGTH], dtype=tf.int32),
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tf.TensorSpec(shape=[BATCH_SIZE, MAX_SEQUENCE_LENGTH], dtype=tf.int32),
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tf.TensorSpec(shape=[BATCH_SIZE, MAX_SEQUENCE_LENGTH], dtype=tf.int32),
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]
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class BertModule(tf.Module):
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def __init__(self):
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super(BertModule, self).__init__()
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# Create a BERT trainer with the created network.
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self.m = TFBertModel.from_pretrained(
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"microsoft/MiniLM-L12-H384-uncased", from_pt=True
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)
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# Invoke the trainer model on the inputs. This causes the layer to be built.
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self.m.predict = lambda x, y, z: self.m.call(
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input_ids=x, attention_mask=y, token_type_ids=z, training=False
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)
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@tf.function(input_signature=bert_input, jit_compile=True)
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def forward(self, input_ids, attention_mask, token_type_ids):
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return self.m.predict(input_ids, attention_mask, token_type_ids)
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if __name__ == "__main__":
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# Prepping Data
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tokenizer = BertTokenizer.from_pretrained(
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"microsoft/MiniLM-L12-H384-uncased"
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)
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text = "Replace me by any text you'd like."
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encoded_input = tokenizer(
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text,
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padding="max_length",
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truncation=True,
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max_length=MAX_SEQUENCE_LENGTH,
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)
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for key in encoded_input:
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encoded_input[key] = tf.expand_dims(
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tf.convert_to_tensor(encoded_input[key]), 0
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)
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test_input = (
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encoded_input["input_ids"],
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encoded_input["attention_mask"],
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encoded_input["token_type_ids"],
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)
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amdshark_module = AMDSharkInference(
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BertModule(), test_input, benchmark_mode=True
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)
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amdshark_module.set_frontend("tensorflow")
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amdshark_module.compile()
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amdshark_module.benchmark_all(test_input)
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