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| """ | |
| 文本 Gradio 部署加载辅助函数。 | |
| 这里绕开训练 Pipeline 的数据源阶段，直接从仓库内的部署模型和词表创建文本推理资源。 | |
| 三个文本 Gradio 页面会复用这些函数，避免 HF Space 启动时访问本地训练数据目录。 | |
| """ | |
| import tensorflow as tf | |
| from deep_learning.data.spec import TokenizerBundle | |
| from deep_learning.data.poetry.tokenizer import load_vectorizer | |
| from deep_learning.data.wiki.tokenizer import sentence_piece | |
| from deep_learning.env.resolve import resolve_saved | |
| from deep_learning.models.mini_gpt import GptModelBuilder | |
| from deep_learning.models.rnn import RNNModelBuilder | |
| from deep_learning.pipeline.services.model_loader import load_inference_artifact | |
| from deep_learning.pipeline.impl.text_generation import ( | |
| TextInferenceBundle, | |
| top_k | |
| ) | |
| def _empty_docs_ds() -> tf.data.Dataset: | |
| return tf.data.Dataset.from_tensor_slices(tf.constant([], dtype=tf.string)) | |
| def _wiki_tokenizer_bundle() -> TokenizerBundle: | |
| vocab_path = resolve_saved("vocab/sentencepiece/vocabulary.proto") | |
| tokenizer, end_of_text, decode = sentence_piece() | |
| return TokenizerBundle( | |
| tokenizer=tokenizer, | |
| decode=decode, | |
| end_of_text=end_of_text, | |
| vocab_size=tokenizer.vocabulary_size(), | |
| vocab_path=str(vocab_path) | |
| ) | |
| def _poetry_tokenizer_bundle(sequence_length: int) -> TokenizerBundle: | |
| vocab_path = resolve_saved("vocab/poetry/vocab.txt") | |
| tokenizer = load_vectorizer(vocab_path, sequence_length + 1) | |
| vocab = tokenizer.get_vocabulary() | |
| end_of_text = vocab.index("$") | |
| def decode(token_ids: list[int]) -> str: | |
| chars = [ | |
| vocab[token_id] for token_id in token_ids if token_id < len(vocab) | |
| ] | |
| return "".join(chars) | |
| return TokenizerBundle( | |
| tokenizer=tokenizer, | |
| decode=decode, | |
| end_of_text=end_of_text, | |
| vocab_size=len(vocab), | |
| vocab_path=str(vocab_path) | |
| ) | |
| def _text_inference_bundle(tokenizer_bundle: TokenizerBundle, max_length: int) -> TextInferenceBundle: | |
| return TextInferenceBundle( | |
| tokenizer_bundle=tokenizer_bundle, | |
| docs_ds=_empty_docs_ds(), | |
| max_length=max_length, | |
| sample_fn=top_k | |
| ) | |
| def load_wiki_gpt_deployment_inference_resource() -> TextInferenceBundle: | |
| return _text_inference_bundle(_wiki_tokenizer_bundle(), 256) | |
| def load_poetry_gpt_deployment_inference_resource() -> TextInferenceBundle: | |
| return _text_inference_bundle(_poetry_tokenizer_bundle(100), 100) | |
| def load_poetry_rnn_deployment_inference_resource() -> TextInferenceBundle: | |
| return _text_inference_bundle(_poetry_tokenizer_bundle(100), 100) | |
| def load_wiki_gpt_deployment_inference_artifact(checkpoint_rule: dict) -> tuple: | |
| model_builder = GptModelBuilder( | |
| hidden_dim=512, | |
| intermediate_dim=2056, | |
| num_heads=8, | |
| num_layers=8 | |
| ) | |
| return load_inference_artifact( | |
| checkpoint_rule, | |
| model_builder.load_inference_artifact, | |
| resource_factory=load_wiki_gpt_deployment_inference_resource | |
| ) | |
| def load_poetry_gpt_deployment_inference_artifact(checkpoint_rule: dict) -> tuple: | |
| model_builder = GptModelBuilder( | |
| hidden_dim=512, | |
| intermediate_dim=2056, | |
| num_heads=4, | |
| num_layers=4 | |
| ) | |
| return load_inference_artifact( | |
| checkpoint_rule, | |
| model_builder.load_inference_artifact, | |
| resource_factory=load_poetry_gpt_deployment_inference_resource | |
| ) | |
| def load_poetry_rnn_deployment_inference_artifact(checkpoint_rule: dict) -> tuple: | |
| model_builder = RNNModelBuilder(num_layers=2, embedding_dim=100, hidden_dim=512) | |
| return load_inference_artifact( | |
| checkpoint_rule, | |
| model_builder.load_inference_artifact, | |
| resource_factory=load_poetry_rnn_deployment_inference_resource | |
| ) | |