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a5fd608 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 | import pytest
import tensorflow as tf
import numpy as np
from models.mini_gpt import GptModelBuilder
from models.rnn import RNNModelBuilder
from pipeline.base.model_builder import GenerationContext
def _sample_one(logits):
return tf.constant([1], dtype="int32")
@pytest.mark.parametrize(
"builder",
[
GptModelBuilder(
hidden_dim=8,
intermediate_dim=16,
num_heads=2,
num_layers=1
),
RNNModelBuilder(
num_layers=1,
embedding_dim=8,
hidden_dim=16
)
]
)
def test_builder_training_and_inference_generate_match(builder):
training_artifact = builder.build_training_artifact(
vocab_size=32,
sequence_length=16
)
inference_artifact = builder.build_inference_artifact(
training_artifact=training_artifact
)
context = GenerationContext(
end_of_text=99,
max_length=6,
sample_fn=_sample_one
)
training_result = training_artifact.generate(context, [2, 3, 4])
inference_result = inference_artifact.generate(context, [2, 3, 4])
assert training_result.token_ids == [2, 3, 4, 1, 1, 1]
assert inference_result.token_ids == training_result.token_ids
assert inference_result.stop_reason == training_result.stop_reason
def test_gpt_inference_artifact_reuses_training_artifact():
builder = GptModelBuilder(
hidden_dim=8,
intermediate_dim=16,
num_heads=2,
num_layers=1
)
training_artifact = builder.build_training_artifact(
vocab_size=32,
sequence_length=16
)
inference_artifact = builder.build_inference_artifact(
training_artifact=training_artifact
)
assert inference_artifact is training_artifact
assert inference_artifact.model is training_artifact.model
def test_rnn_inference_artifact_uses_distinct_model():
builder = RNNModelBuilder(
num_layers=1,
embedding_dim=8,
hidden_dim=16
)
training_artifact = builder.build_training_artifact(
vocab_size=32,
sequence_length=16
)
inference_artifact = builder.build_inference_artifact(
training_artifact=training_artifact
)
assert inference_artifact is not training_artifact
assert inference_artifact.model is not training_artifact.model
def test_rnn_inference_model_outputs_logits_and_states():
builder = RNNModelBuilder(
num_layers=2,
embedding_dim=8,
hidden_dim=16
)
training_artifact = builder.build_training_artifact(
vocab_size=32,
sequence_length=16
)
inference_artifact = builder.build_inference_artifact(
training_artifact=training_artifact
)
token_input = tf.constant([[2, 3, 4]], dtype="int32")
state_inputs = []
for _ in range(builder.num_layers):
state_inputs.append(tf.zeros((1, builder.hidden_dim)))
state_inputs.append(tf.zeros((1, builder.hidden_dim)))
outputs = inference_artifact.model([token_input] + state_inputs, training=False)
assert len(outputs) == 1 + builder.num_layers * 2
assert outputs[0].shape == (1, 32)
for state in outputs[1:]:
assert state.shape == (1, builder.hidden_dim)
def test_rnn_inference_model_copies_training_weights():
builder = RNNModelBuilder(
num_layers=2,
embedding_dim=8,
hidden_dim=16
)
training_artifact = builder.build_training_artifact(
vocab_size=32,
sequence_length=16
)
inference_artifact = builder.build_inference_artifact(
training_artifact=training_artifact
)
training_model = training_artifact.model
inference_model = inference_artifact.model
np.testing.assert_allclose(
training_model.get_layer("embedding").get_weights()[0],
inference_model.get_layer("embedding").get_weights()[0]
)
np.testing.assert_allclose(
training_model.get_layer("logits").get_weights()[0],
inference_model.get_layer("logits").get_weights()[0]
)
np.testing.assert_allclose(
training_model.get_layer("logits").get_weights()[1],
inference_model.get_layer("logits").get_weights()[1]
)
for i in range(builder.num_layers):
training_lstm = training_model.get_layer(f"lstm_{i}")
inference_lstm = inference_model.get_layer(f"lstm_{i}")
for training_weights, inference_weights in zip(
training_lstm.get_weights(),
inference_lstm.get_weights()
):
np.testing.assert_allclose(training_weights, inference_weights)
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