зеркало из https://github.com/microsoft/LightGBM.git
[doc] fix documentation for quantized training (#6528)
fix documentation for quantized training
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@ -680,7 +680,7 @@ Learning Control Parameters
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- gradient quantization can accelerate training, with little accuracy drop in most cases
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- **Note**: can be used only with ``device_type = cpu``
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- **Note**: can be used only with ``device_type = cpu`` and ``device_type=cuda``
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- *New in version 4.0.0*
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@ -690,7 +690,7 @@ Learning Control Parameters
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- with more bins, the quantized training will be closer to full precision training
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- **Note**: can be used only with ``device_type = cpu``
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- **Note**: can be used only with ``device_type = cpu`` and ``device_type=cuda``
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- *New in 4.0.0*
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@ -700,7 +700,7 @@ Learning Control Parameters
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- renewing is very helpful for good quantized training accuracy for ranking objectives
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- **Note**: can be used only with ``device_type = cpu``
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- **Note**: can be used only with ``device_type = cpu`` and ``device_type=cuda``
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- *New in 4.0.0*
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@ -708,6 +708,8 @@ Learning Control Parameters
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- whether to use stochastic rounding in gradient quantization
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- **Note**: can be used only with ``device_type = cpu`` and ``device_type=cuda``
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- *New in 4.0.0*
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IO Parameters
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@ -619,23 +619,24 @@ struct Config {
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// desc = enabling this will discretize (quantize) the gradients and hessians into bins of ``num_grad_quant_bins``
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// desc = with quantized training, most arithmetics in the training process will be integer operations
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// desc = gradient quantization can accelerate training, with little accuracy drop in most cases
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// desc = **Note**: can be used only with ``device_type = cpu``
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// desc = **Note**: can be used only with ``device_type = cpu`` and ``device_type=cuda``
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// desc = *New in version 4.0.0*
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bool use_quantized_grad = false;
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// desc = number of bins to quantization gradients and hessians
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// desc = with more bins, the quantized training will be closer to full precision training
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// desc = **Note**: can be used only with ``device_type = cpu``
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// desc = **Note**: can be used only with ``device_type = cpu`` and ``device_type=cuda``
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// desc = *New in 4.0.0*
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int num_grad_quant_bins = 4;
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// desc = whether to renew the leaf values with original gradients when quantized training
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// desc = renewing is very helpful for good quantized training accuracy for ranking objectives
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// desc = **Note**: can be used only with ``device_type = cpu``
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// desc = **Note**: can be used only with ``device_type = cpu`` and ``device_type=cuda``
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// desc = *New in 4.0.0*
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bool quant_train_renew_leaf = false;
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// desc = whether to use stochastic rounding in gradient quantization
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// desc = **Note**: can be used only with ``device_type = cpu`` and ``device_type=cuda``
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// desc = *New in 4.0.0*
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bool stochastic_rounding = true;
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