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71 lines
1.7 KiB
Python
71 lines
1.7 KiB
Python
import numpy as np
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import torch
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import torchrec
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from tml.common.modules.embedding.config import (
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DataType,
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LargeEmbeddingsConfig,
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)
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from tml.ml_logging.torch_logging import (
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logging,
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)
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from torch import nn
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from torchrec import (
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EmbeddingBagCollection,
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EmbeddingBagConfig,
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)
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from torchrec.modules import (
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embedding_configs,
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)
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from torchrec.sparse.jagged_tensor import (
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KeyedJaggedTensor,
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KeyedTensor,
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)
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class LargeEmbeddings(nn.Module):
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def __init__(
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self,
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large_embeddings_config: LargeEmbeddingsConfig,
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):
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super().__init__()
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tables = []
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for table in large_embeddings_config.tables:
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data_type = (
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embedding_configs.DataType.FP32
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if (table.data_type == DataType.FP32)
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else embedding_configs.DataType.FP16
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)
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tables.append(
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EmbeddingBagConfig(
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embedding_dim=table.embedding_dim,
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feature_names=[table.name], # restricted to 1 feature per table for now
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name=table.name,
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num_embeddings=table.num_embeddings,
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pooling=torchrec.PoolingType.SUM,
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data_type=data_type,
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)
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)
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self.ebc = EmbeddingBagCollection(
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device="meta",
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tables=tables,
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)
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logging.info("********************** EBC named params are **********")
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logging.info(list(self.ebc.named_parameters()))
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# This hook is used to perform post-processing surgery
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# on large_embedding models to prep them for serving
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self.surgery_cut_point = torch.nn.Identity()
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def forward(
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self,
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sparse_features: KeyedJaggedTensor,
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) -> KeyedTensor:
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pooled_embs = self.ebc(sparse_features)
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# a KeyedTensor
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return self.surgery_cut_point(pooled_embs)
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