support dlrm eager train #382
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guo-ran
reviewed
Sep 2, 2022
| save_model("initial_checkpoint") | ||
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| opt = flow.optim.SGD(dlrm_module.parameters(), lr=args.learning_rate) | ||
| lr_scheduler = make_lr_scheduler(args, opt) |
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Suggested change
| lr_scheduler = make_lr_scheduler(args, opt) | |
| lr_scheduler = make_lr_scheduler(args, opt) | |
| opt = flow.one_embedding.Optimizer( | |
| opt, embeddings=[dlrm_module.embedding.one_embedding] | |
| ) |
guo-ran
reviewed
Sep 2, 2022
| test_case=g${num_gpus}_lr${lr}_t${train_batches}_b${train_batch_size}_d${decay_batches} | ||
| echo $test_case | ||
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| export ONEFLOW_ONE_EMBEDDING_EAGER=1 |
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Suggested change
| export ONEFLOW_ONE_EMBEDDING_EAGER=1 |
MARD1NO
reviewed
Oct 8, 2022
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| opt = flow.optim.SGD(dlrm_module.parameters(), lr=args.learning_rate) | ||
| lr_scheduler = make_lr_scheduler(args, opt) | ||
| loss_fn = flow.nn.BCEWithLogitsLoss(reduction="none").to("cuda") |
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这里应该reduction直接设置为mean，才有可能用那个bce fuse kernel
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