mirror of
https://github.com/nod-ai/AMD-SHARK-Studio.git
synced 2026-02-19 11:56:43 -05:00
97 lines
3.4 KiB
Python
97 lines
3.4 KiB
Python
from iree import runtime as ireert
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from iree.tf.support import module_utils
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from iree.compiler import tf as tfc
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from absl import app
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import numpy as np
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import os
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import tensorflow as tf
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from official.nlp.modeling import layers
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from official.nlp.modeling import networks
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from official.nlp.modeling.models import bert_classifier
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vocab_size = 100
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NUM_CLASSES = 5
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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, SEQUENCE_LENGTH], dtype=tf.int32),
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tf.TensorSpec(shape=[BATCH_SIZE, SEQUENCE_LENGTH], dtype=tf.int32),
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tf.TensorSpec(shape=[BATCH_SIZE, 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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dict_outputs = False
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test_network = networks.BertEncoder(
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vocab_size=vocab_size,
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num_layers=24,
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hidden_size=1024,
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num_attention_heads=16,
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dict_outputs=dict_outputs,
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)
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# Create a BERT trainer with the created network.
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bert_trainer_model = bert_classifier.BertClassifier(
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test_network, num_classes=NUM_CLASSES
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)
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bert_trainer_model.summary()
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# Invoke the trainer model on the inputs. This causes the layer to be built.
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self.m = bert_trainer_model
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self.m.predict = lambda x: self.m.call(x, training=False)
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self.m.learn = lambda x, y: self.m.call(x, training=False)
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self.loss = tf.keras.losses.SparseCategoricalCrossentropy()
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self.optimizer = tf.keras.optimizers.SGD(learning_rate=1e-2)
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@tf.function(
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input_signature=[
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tf.TensorSpec(
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shape=[BATCH_SIZE, SEQUENCE_LENGTH], dtype=tf.int32
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), # input0: input_word_ids
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tf.TensorSpec(
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shape=[BATCH_SIZE, SEQUENCE_LENGTH], dtype=tf.int32
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), # input1: input_mask
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tf.TensorSpec(
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shape=[BATCH_SIZE, SEQUENCE_LENGTH], dtype=tf.int32
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), # input2: segment_ids
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tf.TensorSpec([BATCH_SIZE], tf.int32), # input3: labels
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],
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jit_compile=True,
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)
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def learn(self, input_word_ids, input_mask, segment_ids, labels):
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with tf.GradientTape() as tape:
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# Capture the gradients from forward prop...
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inputs = [input_word_ids, input_mask, segment_ids]
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probs = self.m(inputs, training=True)
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loss = self.loss(labels, probs)
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# ...and use them to update the model's weights.
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variables = self.m.trainable_variables
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gradients = tape.gradient(loss, variables)
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self.optimizer.apply_gradients(zip(gradients, variables))
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return loss
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@tf.function(input_signature=bert_input, jit_compile=True)
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def predict(self, input_word_ids, input_mask, segment_ids):
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inputs = [input_word_ids, input_mask, segment_ids]
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return self.m.predict(inputs)
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if __name__ == "__main__":
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# BertModule()
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# Compile the model using IREE
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compiler_module = tfc.compile_module(
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BertModule(), exported_names=["learn"], import_only=True
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)
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# Save module as MLIR file in a directory
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ARITFACTS_DIR = os.getcwd()
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mlir_path = os.path.join(ARITFACTS_DIR, "model.mlir")
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with open(mlir_path, "wt") as output_file:
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output_file.write(compiler_module.decode("utf-8"))
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print(f"Wrote MLIR to path '{mlir_path}'")
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