From cd5c8d180c3c0a760401bd96e52abe28a554e492 Mon Sep 17 00:00:00 2001
From: narawat lamaiin <narawat@outlook.com>
Date: Fri, 15 Mar 2024 08:09:26 +0700
Subject: [PATCH] update

---
 src/interface.jl | 61 ++++++++++++++++++++++++++++++++----------------
 1 file changed, 41 insertions(+), 20 deletions(-)

diff --git a/src/interface.jl b/src/interface.jl
index b1e8503..c3baddc 100644
--- a/src/interface.jl
+++ b/src/interface.jl
@@ -6,37 +6,58 @@ using CUDA
 
 # ------------------------------ 100 characters ------------------------------ #
 
-""" Search wine in stock.
+""" CUDA version of cartesianAssign!().
 
   Arguments\n
-    a : one of ChatAgent's agent.
-    
+  -----
+    a::CuArray
+      target matrix.
+    b::CuArray
+      source matrix. 
+
   Return\n
-    A JSON string of available wine
+  -----
+    Resulting matrix a
 
   Example\n
   ```jldoctest
-  julia> using ChatAgent, CommUtils
-  julia> agent = ChatAgent.agentReflex("Jene")
-  julia> input = "{\"food\": \"pizza\", \"occasion\": \"anniversary\"}"
-  julia> result = winestock(agent, input)
-  "{"wine 1": {\"Winery\": \"Pichon Baron\", \"wine name\": \"Pauillac (Grand Cru Classé)\", \"grape variety\": \"Cabernet Sauvignon\", \"year\": 2010, \"price\": \"125 USD\", \"stock ID\": \"ar-17\"}, }"
+  julia> not done yet
   ```
+
+  Signature\n
+  -----
 """
 cartesianAssign!(a::CuArray, b::CuArray) = @cuda cartesianAssign!(a, b)
 
 
-""" GPU version of batchMatEleMul
-    
-    Example
-    julia> using Flux, CUDA
-    julia> device = Flux.CUDA.functional() ? gpu : cpu
-    julia> if device == gpu    CUDA.device!(0) end 
-    julia> input = rand(32, 32, 128)    |> gpu;      # 128-batches
-    julia> weight = rand(32, 32, 1024)  |> gpu;      # 1-batch. this matrix is essentially (32, 32, 1024, 1)
-    julia> r = matMul_3Dto3D_manyTo1batch(input, weight);
-    julia> size(r)
-    (32, 32, 1024, 128)
+""" CUDA version of batchMatEleMul.
+
+  Arguments\n
+  -----
+    a::CuArray 
+      Input matrix
+    b::CuArray 
+      Input matrix
+
+  Return\n
+  -----
+    Resulting matrix
+
+  Example\n
+  -----
+  ```jldoctest
+  julia> using Flux, CUDA
+  julia> device = Flux.CUDA.functional() ? gpu : cpu
+  julia> if device == gpu    CUDA.device!(0) end 
+  julia> input = rand(32, 32, 128)    |> gpu;      # 128-batches
+  julia> weight = rand(32, 32, 1024)  |> gpu;      # 1-batch. this matrix is essentially (32, 32, 1024, 1)
+  julia> r = matMul_3Dto3D_manyTo1batch(input, weight);
+  julia> size(r)
+  (32, 32, 1024, 128)
+  ```
+  
+  Signature\n
+  -----
 """
 function matMul_3Dto3D_manyTo1batch(a::CuArray, b::CuArray; 
                         resultStorage::Union{CuArray, Nothing}=nothing)