blink/biencoder/train_biencoder.py [117:145]:
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    tokenizer = reranker.tokenizer
    model = reranker.model

    device = reranker.device
    n_gpu = reranker.n_gpu

    if params["gradient_accumulation_steps"] < 1:
        raise ValueError(
            "Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format(
                params["gradient_accumulation_steps"]
            )
        )

    # An effective batch size of `x`, when we are accumulating the gradient accross `y` batches will be achieved by having a batch size of `z = x / y`
    # args.gradient_accumulation_steps = args.gradient_accumulation_steps // n_gpu
    params["train_batch_size"] = (
        params["train_batch_size"] // params["gradient_accumulation_steps"]
    )
    train_batch_size = params["train_batch_size"]
    eval_batch_size = params["eval_batch_size"]
    grad_acc_steps = params["gradient_accumulation_steps"]

    # Fix the random seeds
    seed = params["seed"]
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    if reranker.n_gpu > 0:
        torch.cuda.manual_seed_all(seed)
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blink/crossencoder/train_cross.py [167:197]:
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    tokenizer = reranker.tokenizer
    model = reranker.model

    # utils.save_model(model, tokenizer, model_output_path)

    device = reranker.device
    n_gpu = reranker.n_gpu

    if params["gradient_accumulation_steps"] < 1:
        raise ValueError(
            "Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format(
                params["gradient_accumulation_steps"]
            )
        )

    # An effective batch size of `x`, when we are accumulating the gradient accross `y` batches will be achieved by having a batch size of `z = x / y`
    # args.gradient_accumulation_steps = args.gradient_accumulation_steps // n_gpu
    params["train_batch_size"] = (
        params["train_batch_size"] // params["gradient_accumulation_steps"]
    )
    train_batch_size = params["train_batch_size"]
    eval_batch_size = params["eval_batch_size"]
    grad_acc_steps = params["gradient_accumulation_steps"]

    # Fix the random seeds
    seed = params["seed"]
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    if reranker.n_gpu > 0:
        torch.cuda.manual_seed_all(seed)
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