60 lines
1.8 KiB
Python
60 lines
1.8 KiB
Python
from typing import List
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from enum import Enum
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import tml.core.config as base_config
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from tml.optimizers.config import OptimizerConfig
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import pydantic
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class DataType(str, Enum):
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FP32 = "fp32"
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FP16 = "fp16"
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class EmbeddingSnapshot(base_config.BaseConfig):
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"""Configuration for Embedding snapshot"""
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emb_name: str = pydantic.Field(
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..., description="Name of the embedding table from the loaded snapshot"
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)
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embedding_snapshot_uri: str = pydantic.Field(
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..., description="Path to torchsnapshot of the embedding"
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)
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class EmbeddingBagConfig(base_config.BaseConfig):
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"""Configuration for EmbeddingBag."""
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name: str = pydantic.Field(..., description="name of embedding bag")
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num_embeddings: int = pydantic.Field(..., description="size of embedding dictionary")
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embedding_dim: int = pydantic.Field(..., description="size of each embedding vector")
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pretrained: EmbeddingSnapshot = pydantic.Field(None, description="Snapshot properties")
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vocab: str = pydantic.Field(
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None, description="Directory to parquet files of mapping from entity ID to table index."
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)
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# make sure to use an optimizer that matches:
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# https://github.com/pytorch/FBGEMM/blob/4c58137529d221390575e47e88d3c05ce65b66fd/fbgemm_gpu/fbgemm_gpu/split_embedding_configs.py#L15
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optimizer: OptimizerConfig
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data_type: DataType
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class LargeEmbeddingsConfig(base_config.BaseConfig):
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"""Configuration for EmbeddingBagCollection.
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The tables listed in this config are gathered into a single torchrec EmbeddingBagCollection.
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"""
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tables: List[EmbeddingBagConfig] = pydantic.Field(..., description="list of embedding tables")
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tables_to_log: List[str] = pydantic.Field(
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None, description="list of embedding table names that we want to log during training"
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)
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class Mode(str, Enum):
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"""Job modes."""
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TRAIN = "train"
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EVALUATE = "evaluate"
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INFERENCE = "inference"
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