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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 | from dataclasses import dataclass
import pathlib
import numpy as np
import tensorflow as tf
from data.base import DataBundle, TokenizerBundle
from models.mini_gpt import GptModelBuilder
from models.rnn import RNNModelBuilder
from pipeline.base.configs import CheckpointConfig, CheckpointRules, GenerationRule, TrainingRule
from pipeline.base.model_loader import (
load_inference_artifact_from_pipeline,
load_training_artifact_from_pipeline
)
from pipeline.pipeline import Pipeline
@dataclass
class DummyDataset(DataBundle):
def doc_ds(self) -> tf.data.Dataset:
return tf.data.Dataset.from_tensor_slices(["abc"])
def tokens_ds(self, seq_length: int, batch_size: int) -> tf.data.Dataset:
inputs = tf.constant([[1, 2, 3]], dtype="int32")
targets = tf.constant([[2, 3, 4]], dtype="int32")
return tf.data.Dataset.from_tensor_slices((inputs, targets)).batch(batch_size)
def tokenizer_bundle(self) -> TokenizerBundle:
return TokenizerBundle(
tokenizer=lambda text: tf.constant([1, 2, 3], dtype="int32"),
decode=lambda ids: "".join(str(token) for token in ids),
end_of_text=99,
vocab_size=32
)
def _sample_one(logits):
return tf.constant([1], dtype="int32")
def _create_pipeline(
task_dir: pathlib.Path,
model_builder,
checkpoint_path: pathlib.Path
) -> Pipeline:
return Pipeline(
name="test_task",
dataset=DummyDataset(data_dir="unused", sequence_length=16),
model_builder=model_builder,
training_rule=TrainingRule(batch_size=1, epochs=1, steps_per_epoch=1, validation_batches=1),
generation_rule=GenerationRule(
prompts_generator=lambda dataset: ["abc"],
sample_strategy=_sample_one
),
checkpoint_rules=CheckpointRules(
testing=CheckpointConfig(path=checkpoint_path)
),
task_dir=task_dir
)
def _save_training_checkpoint(model_builder, checkpoint_path: pathlib.Path):
training_artifact = model_builder.build_training_artifact(
vocab_size=32,
sequence_length=16
)
checkpoint_path.parent.mkdir(parents=True, exist_ok=True)
if checkpoint_path.suffix.lower() == ".keras":
training_artifact.model.save(str(checkpoint_path))
else:
training_artifact.model.save_weights(str(checkpoint_path))
return training_artifact
def test_load_training_artifact_from_keras_checkpoint(tmp_path):
builder = GptModelBuilder(
hidden_dim=8,
intermediate_dim=16,
num_heads=2,
num_layers=1
)
pipeline = _create_pipeline(
tmp_path / "task",
builder,
pathlib.Path("model_epoch_003.keras")
)
saved_artifact = _save_training_checkpoint(
builder,
pipeline.checkpoint_dir / "model_epoch_003.keras"
)
checkpoint_rule = pipeline.checkpoint_rules.resolve_testing_rule(
default_dirs=[pipeline.checkpoint_dir]
)
loaded_artifact, tokenizer_info = load_training_artifact_from_pipeline(
pipeline,
checkpoint_rule
)
assert loaded_artifact.model.name == "mini_gpt"
assert tokenizer_info.vocab_size == 32
for saved_weights, loaded_weights in zip(
saved_artifact.model.get_weights(),
loaded_artifact.model.get_weights()
):
np.testing.assert_allclose(saved_weights, loaded_weights)
def test_load_training_artifact_from_weights_checkpoint(tmp_path):
builder = RNNModelBuilder(
num_layers=1,
embedding_dim=8,
hidden_dim=16
)
pipeline = _create_pipeline(
tmp_path / "task",
builder,
pathlib.Path("model_epoch_003.weights.h5")
)
saved_artifact = _save_training_checkpoint(
builder,
pipeline.checkpoint_dir / "model_epoch_003.weights.h5"
)
checkpoint_rule = pipeline.checkpoint_rules.resolve_testing_rule(
default_dirs=[pipeline.checkpoint_dir]
)
loaded_artifact, tokenizer_info = load_training_artifact_from_pipeline(
pipeline,
checkpoint_rule
)
assert loaded_artifact.model.name == "rnn_training"
assert tokenizer_info.vocab_size == 32
for saved_weights, loaded_weights in zip(
saved_artifact.model.get_weights(),
loaded_artifact.model.get_weights()
):
np.testing.assert_allclose(saved_weights, loaded_weights)
def test_load_inference_artifact_from_pipeline_returns_gpt_model(tmp_path):
builder = GptModelBuilder(
hidden_dim=8,
intermediate_dim=16,
num_heads=2,
num_layers=1
)
pipeline = _create_pipeline(
tmp_path / "task",
builder,
pathlib.Path("model_epoch_003.keras")
)
_save_training_checkpoint(
builder,
pipeline.checkpoint_dir / "model_epoch_003.keras"
)
checkpoint_rule = pipeline.checkpoint_rules.resolve_testing_rule(
default_dirs=[pipeline.checkpoint_dir]
)
inference_artifact, _ = load_inference_artifact_from_pipeline(
pipeline,
checkpoint_rule
)
outputs = inference_artifact.model(tf.constant([[2, 3, 4]], dtype="int32"), training=False)
assert outputs.shape == (1, 3, 32)
def test_load_inference_artifact_from_pipeline_returns_rnn_model(tmp_path):
builder = RNNModelBuilder(
num_layers=1,
embedding_dim=8,
hidden_dim=16
)
pipeline = _create_pipeline(
tmp_path / "task",
builder,
pathlib.Path("model_epoch_003.weights.h5")
)
_save_training_checkpoint(
builder,
pipeline.checkpoint_dir / "model_epoch_003.weights.h5"
)
checkpoint_rule = pipeline.checkpoint_rules.resolve_testing_rule(
default_dirs=[pipeline.checkpoint_dir]
)
inference_artifact, _ = load_inference_artifact_from_pipeline(
pipeline,
checkpoint_rule
)
outputs = inference_artifact.model(
[
tf.constant([[2, 3, 4]], dtype="int32"),
tf.zeros((1, 16)),
tf.zeros((1, 16))
],
training=False
)
assert len(outputs) == 3
assert outputs[0].shape == (1, 32)
assert outputs[1].shape == (1, 16)
assert outputs[2].shape == (1, 16)
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