From fc788a51b61368f78246e5c450f0ea77d263eb2b Mon Sep 17 00:00:00 2001
From: shiyu1994 <shiyu_k1994@qq.com>
Date: Tue, 9 Jul 2024 10:24:54 +0800
Subject: [PATCH] [doc] fix documentation for quantized training (#6528)

fix documentation for quantized training
---
 docs/Parameters.rst       | 8 +++++---
 include/LightGBM/config.h | 7 ++++---
 2 files changed, 9 insertions(+), 6 deletions(-)

diff --git a/docs/Parameters.rst b/docs/Parameters.rst
index 40551cfd5858..bd74bba463d1 100644
--- a/docs/Parameters.rst
+++ b/docs/Parameters.rst
@@ -680,7 +680,7 @@ Learning Control Parameters
 
    -  gradient quantization can accelerate training, with little accuracy drop in most cases
 
-   -  **Note**: can be used only with ``device_type = cpu``
+   -  **Note**: can be used only with ``device_type = cpu`` and ``device_type=cuda``
 
    -  *New in version 4.0.0*
 
@@ -690,7 +690,7 @@ Learning Control Parameters
 
    -  with more bins, the quantized training will be closer to full precision training
 
-   -  **Note**: can be used only with ``device_type = cpu``
+   -  **Note**: can be used only with ``device_type = cpu`` and ``device_type=cuda``
 
    -  *New in 4.0.0*
 
@@ -700,7 +700,7 @@ Learning Control Parameters
 
    -  renewing is very helpful for good quantized training accuracy for ranking objectives
 
-   -  **Note**: can be used only with ``device_type = cpu``
+   -  **Note**: can be used only with ``device_type = cpu`` and ``device_type=cuda``
 
    -  *New in 4.0.0*
 
@@ -708,6 +708,8 @@ Learning Control Parameters
 
    -  whether to use stochastic rounding in gradient quantization
 
+   -  **Note**: can be used only with ``device_type = cpu`` and ``device_type=cuda``
+
    -  *New in 4.0.0*
 
 IO Parameters
diff --git a/include/LightGBM/config.h b/include/LightGBM/config.h
index a2a352669460..ed3dd1226e1a 100644
--- a/include/LightGBM/config.h
+++ b/include/LightGBM/config.h
@@ -619,23 +619,24 @@ struct Config {
   // desc = enabling this will discretize (quantize) the gradients and hessians into bins of ``num_grad_quant_bins``
   // desc = with quantized training, most arithmetics in the training process will be integer operations
   // desc = gradient quantization can accelerate training, with little accuracy drop in most cases
-  // desc = **Note**: can be used only with ``device_type = cpu``
+  // desc = **Note**: can be used only with ``device_type = cpu`` and ``device_type=cuda``
   // desc = *New in version 4.0.0*
   bool use_quantized_grad = false;
 
   // desc = number of bins to quantization gradients and hessians
   // desc = with more bins, the quantized training will be closer to full precision training
-  // desc = **Note**: can be used only with ``device_type = cpu``
+  // desc = **Note**: can be used only with ``device_type = cpu`` and ``device_type=cuda``
   // desc = *New in 4.0.0*
   int num_grad_quant_bins = 4;
 
   // desc = whether to renew the leaf values with original gradients when quantized training
   // desc = renewing is very helpful for good quantized training accuracy for ranking objectives
-  // desc = **Note**: can be used only with ``device_type = cpu``
+  // desc = **Note**: can be used only with ``device_type = cpu`` and ``device_type=cuda``
   // desc = *New in 4.0.0*
   bool quant_train_renew_leaf = false;
 
   // desc = whether to use stochastic rounding in gradient quantization
+  // desc = **Note**: can be used only with ``device_type = cpu`` and ``device_type=cuda``
   // desc = *New in 4.0.0*
   bool stochastic_rounding = true;