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docs/docs_en/api/function.md
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docs/docs_en/api/function.md
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# FDTensor C++ Tensor quantization function
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FDTensor is FastDeploy's struct that represents the tensor at the C++ level. The struct is mainly used to manage the input and output data of the model during inference deployment and supports different Runtime backends.
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In the development of C++-based inference deployment applications, developers often need to process some data on the input and output to get the actual input or the actual output of the application. This pre-processing data logic can easily be done by the original C++ standard library. But it can be difficult to develop, e.g. to find the maximum value for the 2nd dimension of a 3-dimensional Tensor. To solve this problem, FastDeploy has developed a set of C++ tensor functions based on FDTensor to reduce costs and increase efficiency for FastDeploy developers. There are currently four main functions: Reduce, Manipulate, Math and Elementwise.
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## Reduce Class Function
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Currently FastDeploy supports 7 types of Reduce class functions ：Max, Min, Sum, All, Any, Mean, Prod.
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### Max
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#### Function Signature
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```c++
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/** Excute the maximum operation for input FDTensor along given dims.
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@param x The input tensor.
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@param out The output tensor which stores the result.
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@param dims The vector of axis which will be reduced.
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@param keep_dim Whether to keep the reduced dims, default false.
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@param reduce_all Whether to reduce all dims, default false.
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*/
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void Max(const FDTensor& x, FDTensor* out,
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const std::vector<int64_t>& dims,
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bool keep_dim = false, bool reduce_all = false);
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```
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#### Demo
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```c++
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FDTensor input, output;
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std::vector<int> inputs = {2, 4, 3, 7, 1, 5};
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input.SetExternalData({2, 3}, FDDataType::INT32, inputs.data());
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// Calculate the max value for axis 0 of `inputs`
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// The output result would be [[7, 4, 5]].
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Max(input, &output, {0}, /* keep_dim = */true);
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```
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### Min
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#### Function Signature
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```c++
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/** Excute the minimum operation for input FDTensor along given dims.
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@param x The input tensor.
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@param out The output tensor which stores the result.
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@param dims The vector of axis which will be reduced.
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@param keep_dim Whether to keep the reduced dims, default false.
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@param reduce_all Whether to reduce all dims, default false.
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*/
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void Min(const FDTensor& x, FDTensor* out,
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const std::vector<int64_t>& dims,
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bool keep_dim = false, bool reduce_all = false);
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```
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#### Demo
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```c++
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FDTensor input, output;
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std::vector<int> inputs = {2, 4, 3, 7, 1, 5};
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input.SetExternalData({2, 3}, FDDataType::INT32, inputs.data());
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// Calculate the min value for axis 0 of `inputs`
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// The output result would be [[2, 1, 3]].
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Min(input, &output, {0}, /* keep_dim = */true);
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```
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### Sum
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#### Function Signature
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```c++
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/** Excute the sum operation for input FDTensor along given dims.
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@param x The input tensor.
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@param out The output tensor which stores the result.
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@param dims The vector of axis which will be reduced.
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@param keep_dim Whether to keep the reduced dims, default false.
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@param reduce_all Whether to reduce all dims, default false.
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*/
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void Sum(const FDTensor& x, FDTensor* out,
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const std::vector<int64_t>& dims,
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bool keep_dim = false, bool reduce_all = false);
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```
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#### Demo
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```c++
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FDTensor input, output;
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std::vector<int> inputs = {2, 4, 3, 7, 1, 5};
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input.SetExternalData({2, 3}, FDDataType::INT32, inputs.data());
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// Calculate the sum value for axis 0 of `inputs`
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// The output result would be [[9, 5, 8]].
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Sum(input, &output, {0}, /* keep_dim = */true);
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```
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### Mean
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#### Function Signature
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```c++
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/** Excute the mean operation for input FDTensor along given dims.
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@param x The input tensor.
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@param out The output tensor which stores the result.
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@param dims The vector of axis which will be reduced.
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@param keep_dim Whether to keep the reduced dims, default false.
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@param reduce_all Whether to reduce all dims, default false.
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*/
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void Mean(const FDTensor& x, FDTensor* out,
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const std::vector<int64_t>& dims,
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bool keep_dim = false, bool reduce_all = false);
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```
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#### Demo
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```c++
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FDTensor input, output;
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std::vector<int> inputs = {2, 4, 3, 7, 1, 5};
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input.SetExternalData({2, 3}, FDDataType::INT32, inputs.data());
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// Calculate the mean value for axis 0 of `inputs`
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// The output result would be [[4, 2, 4]].
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Mean(input, &output, {0}, /* keep_dim = */true);
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```
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### Prod
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#### Function Signature
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```c++
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/** Excute the product operation for input FDTensor along given dims.
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@param x The input tensor.
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@param out The output tensor which stores the result.
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@param dims The vector of axis which will be reduced.
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@param keep_dim Whether to keep the reduced dims, default false.
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@param reduce_all Whether to reduce all dims, default false.
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*/
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void Prod(const FDTensor& x, FDTensor* out,
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const std::vector<int64_t>& dims,
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bool keep_dim = false, bool reduce_all = false);
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```
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#### Demo
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```c++
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FDTensor input, output;
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std::vector<int> inputs = {2, 4, 3, 7, 1, 5};
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input.SetExternalData({2, 3}, FDDataType::INT32, inputs.data());
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// Calculate the product value for axis 0 of `inputs`
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// The output result would be [[14, 4, 15]].
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Prod(input, &output, {0}, /* keep_dim = */true);
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```
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### Any
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#### Function Signature
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```c++
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/** Excute the any operation for input FDTensor along given dims.
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@param x The input tensor.
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@param out The output tensor which stores the result.
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@param dims The vector of axis which will be reduced.
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@param keep_dim Whether to keep the reduced dims, default false.
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@param reduce_all Whether to reduce all dims, default false.
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*/
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void Any(const FDTensor& x, FDTensor* out,
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const std::vector<int64_t>& dims,
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bool keep_dim = false, bool reduce_all = false);
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```
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#### Demo
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```c++
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FDTensor input, output;
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std::array<bool, 6> bool_inputs = {false, false, true, true, false, true};
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input.SetExternalData({2, 3}, FDDataType::INT32, bool_inputs.data());
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// Calculate the any value for axis 0 of `inputs`
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// The output result would be [[true, false, true]].
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Any(input, &output, {0}, /* keep_dim = */true);
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```
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### All
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#### Function Signature
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```c++
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/** Excute the all operation for input FDTensor along given dims.
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@param x The input tensor.
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@param out The output tensor which stores the result.
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@param dims The vector of axis which will be reduced.
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@param keep_dim Whether to keep the reduced dims, default false.
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@param reduce_all Whether to reduce all dims, default false.
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*/
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void All(const FDTensor& x, FDTensor* out,
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const std::vector<int64_t>& dims,
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bool keep_dim = false, bool reduce_all = false);
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```
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#### Demo
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```c++
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FDTensor input, output;
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std::array<bool, 6> bool_inputs = {false, false, true, true, false, true};
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input.SetExternalData({2, 3}, FDDataType::INT32, bool_inputs.data());
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// Calculate the all value for axis 0 of `inputs`
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// The output result would be [[false, false, true]].
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All(input, &output, {0}, /* keep_dim = */true);
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```
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## Manipulate Class Function
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Currently FastDeploy supports 1 Manipulate class function: Transpose.
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### Transpose
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#### Function Signature
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```c++
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/** Excute the transpose operation for input FDTensor along given dims.
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@param x The input tensor.
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@param out The output tensor which stores the result.
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@param dims The vector of axis which the input tensor will transpose.
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*/
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void Transpose(const FDTensor& x, FDTensor* out,
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const std::vector<int64_t>& dims);
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```
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#### Demo
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```c++
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FDTensor input, output;
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std::vector<float> inputs = {2, 4, 3, 7, 1, 5};
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input.SetExternalData({2, 3}, FDDataType::FP32, inputs.data());
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// Transpose the input tensor with axis {1, 0}.
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// The output result would be [[2, 7], [4, 1], [3, 5]]
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Transpose(input, &output, {1, 0});
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```
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## Math Class Function
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Currently FastDeploy supports 1 Math class function: Softmax.
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### Softmax
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#### Function Signature
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```c++
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/** Excute the softmax operation for input FDTensor along given dims.
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@param x The input tensor.
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@param out The output tensor which stores the result.
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@param axis The axis to be computed softmax value.
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*/
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void Softmax(const FDTensor& x, FDTensor* out, int axis = -1);
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```
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#### Demo
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```c++
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FDTensor input, output;
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CheckShape check_shape;
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CheckData check_data;
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std::vector<float> inputs = {1, 2, 3, 4, 5, 6};
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input.SetExternalData({2, 3}, FDDataType::FP32, inputs.data());
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// Transpose the input tensor with axis {1, 0}.
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// The output result would be
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// [[0.04742587, 0.04742587, 0.04742587],
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// [0.95257413, 0.95257413, 0.95257413]]
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Softmax(input, &output, 0);
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```
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## Elementwise Class Function
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To be continued...
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