COMPMID-627 Create CL SGEMM example

Change-Id: I17478202348d34716d00c78e3cc21254c76b32e1
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/93304
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

1 files changed, 334 insertions, 44 deletions

diff --git a/utils/Utils.h b/utils/Utils.h
index c88de0e16b..1f3d971917 100644
--- a/utils/Utils.h
+++ b/utils/Utils.h
@@ -30,6 +30,7 @@
 #include "arm_compute/core/Validate.h"
 #include "arm_compute/core/Window.h"
 #include "arm_compute/runtime/Tensor.h"
+#include "libnpy/npy.hpp"
 #include "support/ToolchainSupport.h"
 
 #ifdef ARM_COMPUTE_CL
@@ -41,6 +42,10 @@
 #include <cstring>
 #include <fstream>
 #include <iostream>
+#include <random>
+#include <string>
+#include <tuple>
+#include <vector>
 
 namespace arm_compute
 {
@@ -80,6 +85,66 @@ void draw_detection_rectangle(arm_compute::ITensor *tensor, const arm_compute::D
  */
 std::tuple<unsigned int, unsigned int, int> parse_ppm_header(std::ifstream &fs);
 
+/** Parse the npy header from an input file stream. At the end of the execution,
+ *  the file position pointer will be located at the first pixel stored in the npy file //TODO
+ *
+ * @param[in] fs Input file stream to parse
+ *
+ * @return The width and height stored in the header of the NPY file
+ */
+std::tuple<std::vector<unsigned long>, bool, std::string> parse_npy_header(std::ifstream &fs);
+
+/** Obtain numpy type string from DataType.
+ *
+ * @param[in] data_type Data type.
+ *
+ * @return numpy type string.
+ */
+inline std::string get_typestring(DataType data_type)
+{
+    // Check endianness
+    const unsigned int i = 1;
+    const char        *c = reinterpret_cast<const char *>(&i);
+    std::string        endianness;
+    if(*c == 1)
+    {
+        endianness = std::string("<");
+    }
+    else
+    {
+        endianness = std::string(">");
+    }
+    const std::string no_endianness("|");
+
+    switch(data_type)
+    {
+        case DataType::U8:
+            return no_endianness + "u" + support::cpp11::to_string(sizeof(uint8_t));
+        case DataType::S8:
+            return no_endianness + "i" + support::cpp11::to_string(sizeof(int8_t));
+        case DataType::U16:
+            return endianness + "u" + support::cpp11::to_string(sizeof(uint16_t));
+        case DataType::S16:
+            return endianness + "i" + support::cpp11::to_string(sizeof(int16_t));
+        case DataType::U32:
+            return endianness + "u" + support::cpp11::to_string(sizeof(uint32_t));
+        case DataType::S32:
+            return endianness + "i" + support::cpp11::to_string(sizeof(int32_t));
+        case DataType::U64:
+            return endianness + "u" + support::cpp11::to_string(sizeof(uint64_t));
+        case DataType::S64:
+            return endianness + "i" + support::cpp11::to_string(sizeof(int64_t));
+        case DataType::F32:
+            return endianness + "f" + support::cpp11::to_string(sizeof(float));
+        case DataType::F64:
+            return endianness + "f" + support::cpp11::to_string(sizeof(double));
+        case DataType::SIZET:
+            return endianness + "u" + support::cpp11::to_string(sizeof(size_t));
+        default:
+            ARM_COMPUTE_ERROR("NOT SUPPORTED!");
+    }
+}
+
 /** Maps a tensor if needed
  *
  * @param[in] tensor   Tensor to be mapped
@@ -350,6 +415,159 @@ private:
     unsigned int  _width, _height;
 };
 
+class NPYLoader
+{
+public:
+    NPYLoader()
+        : _fs(), _shape(), _fortran_order(false), _typestring()
+    {
+    }
+
+    /** Open a NPY file and reads its metadata
+     *
+     * @param[in] npy_filename File to open
+     */
+    void open(const std::string &npy_filename)
+    {
+        ARM_COMPUTE_ERROR_ON(is_open());
+        try
+        {
+            _fs.exceptions(std::ifstream::failbit | std::ifstream::badbit);
+            _fs.open(npy_filename, std::ios::in | std::ios::binary);
+
+            std::tie(_shape, _fortran_order, _typestring) = parse_npy_header(_fs);
+        }
+        catch(const std::ifstream::failure &e)
+        {
+            ARM_COMPUTE_ERROR("Accessing %s: %s", npy_filename.c_str(), e.what());
+        }
+    }
+    /** Return true if a NPY file is currently open */
+    bool is_open()
+    {
+        return _fs.is_open();
+    }
+
+    /** Return true if a NPY file is in fortran order */
+    bool is_fortran()
+    {
+        return _fortran_order;
+    }
+
+    /** Initialise an image's metadata with the dimensions of the NPY file currently open
+     *
+     * @param[out] tensor Tensor to initialise
+     * @param[in]  format Format to use for the image
+     */
+    template <typename T>
+    void init_tensor(T &tensor, arm_compute::Format format)
+    {
+        ARM_COMPUTE_ERROR_ON(!is_open());
+        ARM_COMPUTE_ERROR_ON(format != arm_compute::Format::F32);
+
+        // Use the size of the input NPY tensor
+        TensorShape shape;
+        shape.set_num_dimensions(_shape.size());
+        for(size_t i = 0; i < _shape.size(); ++i)
+        {
+            shape.set(i, _shape.at(i));
+        }
+
+        arm_compute::TensorInfo tensor_info(shape, format);
+        tensor.allocator()->init(tensor_info);
+    }
+
+    /** Fill a tensor with the content of the currently open NPY file.
+     *
+     * @note If the tensor is a CLTensor, the function maps and unmaps the tensor
+     *
+     * @param[in,out] tensor Tensor to fill (Must be allocated, and of matching dimensions with the opened NPY).
+     */
+    template <typename T>
+    void fill_tensor(T &tensor)
+    {
+        ARM_COMPUTE_ERROR_ON(!is_open());
+        ARM_COMPUTE_ERROR_ON_FORMAT_NOT_IN(&tensor, arm_compute::Format::F32);
+        try
+        {
+            // Map buffer if creating a CLTensor
+            map(tensor, true);
+
+            // Check if the file is large enough to fill the tensor
+            const size_t current_position = _fs.tellg();
+            _fs.seekg(0, std::ios_base::end);
+            const size_t end_position = _fs.tellg();
+            _fs.seekg(current_position, std::ios_base::beg);
+
+            ARM_COMPUTE_ERROR_ON_MSG((end_position - current_position) < tensor.info()->tensor_shape().total_size() * tensor.info()->element_size(),
+                                     "Not enough data in file");
+            ARM_COMPUTE_UNUSED(end_position);
+
+            // Check if the typestring matches the given one
+            std::string expect_typestr = get_typestring(tensor.info()->data_type());
+            ARM_COMPUTE_ERROR_ON_MSG(_typestring != expect_typestr, "Typestrings mismatch");
+
+            // Validate tensor shape
+            ARM_COMPUTE_ERROR_ON_MSG(_shape.size() != tensor.shape().num_dimensions(), "Tensor ranks mismatch");
+            if(_fortran_order)
+            {
+                for(size_t i = 0; i < _shape.size(); ++i)
+                {
+                    ARM_COMPUTE_ERROR_ON_MSG(tensor.shape()[i] != _shape[i], "Tensor dimensions mismatch");
+                }
+            }
+            else
+            {
+                for(size_t i = 0; i < _shape.size(); ++i)
+                {
+                    ARM_COMPUTE_ERROR_ON_MSG(tensor.shape()[i] != _shape[_shape.size() - i - 1], "Tensor dimensions mismatch");
+                }
+            }
+
+            switch(tensor.info()->format())
+            {
+                case arm_compute::Format::F32:
+                {
+                    // Read data
+                    if(tensor.info()->padding().empty())
+                    {
+                        // If tensor has no padding read directly from stream.
+                        _fs.read(reinterpret_cast<char *>(tensor.buffer()), tensor.info()->total_size());
+                    }
+                    else
+                    {
+                        // If tensor has padding accessing tensor elements through execution window.
+                        Window window;
+                        window.use_tensor_dimensions(tensor.info()->tensor_shape());
+
+                        execute_window_loop(window, [&](const Coordinates & id)
+                        {
+                            _fs.read(reinterpret_cast<char *>(tensor.ptr_to_element(id)), tensor.info()->element_size());
+                        });
+                    }
+
+                    break;
+                }
+                default:
+                    ARM_COMPUTE_ERROR("Unsupported format");
+            }
+
+            // Unmap buffer if creating a CLTensor
+            unmap(tensor);
+        }
+        catch(const std::ifstream::failure &e)
+        {
+            ARM_COMPUTE_ERROR("Loading NPY file: %s", e.what());
+        }
+    }
+
+private:
+    std::ifstream              _fs;
+    std::vector<unsigned long> _shape;
+    bool                       _fortran_order;
+    std::string                _typestring;
+};
+
 /** Template helper function to save a tensor image to a PPM file.
  *
  * @note Only U8 and RGB888 formats supported.
@@ -430,6 +648,83 @@ void save_to_ppm(T &tensor, const std::string &ppm_filename)
     }
 }
 
+/** Template helper function to save a tensor image to a NPY file.
+ *
+ * @note Only F32 format supported.
+ * @note Only works with 2D tensors.
+ * @note If the input tensor is a CLTensor, the function maps and unmaps the image
+ *
+ * @param[in] tensor        The tensor to save as NPY file
+ * @param[in] npy_filename  Filename of the file to create.
+ * @param[in] fortran_order If true, save matrix in fortran order.
+ */
+template <typename T>
+void save_to_npy(T &tensor, const std::string &npy_filename, bool fortran_order)
+{
+    ARM_COMPUTE_ERROR_ON_FORMAT_NOT_IN(&tensor, arm_compute::Format::F32);
+    ARM_COMPUTE_ERROR_ON(tensor.info()->num_dimensions() > 2);
+
+    std::ofstream fs;
+
+    try
+    {
+        fs.exceptions(std::ofstream::failbit | std::ofstream::badbit | std::ofstream::eofbit);
+        fs.open(npy_filename, std::ios::out | std::ios::binary);
+
+        const unsigned int width  = tensor.info()->tensor_shape()[0];
+        const unsigned int height = tensor.info()->tensor_shape()[1];
+        unsigned long      shape[2];
+
+        if(!fortran_order)
+        {
+            shape[0] = height, shape[1] = width;
+        }
+        else
+        {
+            shape[0] = width, shape[1] = height;
+        }
+
+        // Map buffer if creating a CLTensor
+        map(tensor, true);
+
+        switch(tensor.info()->format())
+        {
+            case arm_compute::Format::F32:
+            {
+                std::vector<float> tmp; /* Used only to get the typestring */
+                npy::Typestring    typestring_o{ tmp };
+                std::string        typestring = typestring_o.str();
+
+                std::ofstream stream(npy_filename, std::ofstream::binary);
+                npy::WriteHeader(stream, typestring, fortran_order, 2, shape);
+
+                arm_compute::Window window;
+                window.set(arm_compute::Window::DimX, arm_compute::Window::Dimension(0, width, 1));
+                window.set(arm_compute::Window::DimY, arm_compute::Window::Dimension(0, height, 1));
+
+                arm_compute::Iterator in(&tensor, window);
+
+                arm_compute::execute_window_loop(window, [&](const arm_compute::Coordinates & id)
+                {
+                    stream.write(reinterpret_cast<const char *>(in.ptr()), sizeof(float));
+                },
+                in);
+
+                break;
+            }
+            default:
+                ARM_COMPUTE_ERROR("Unsupported format");
+        }
+
+        // Unmap buffer if creating a CLTensor
+        unmap(tensor);
+    }
+    catch(const std::ofstream::failure &e)
+    {
+        ARM_COMPUTE_ERROR("Writing %s: (%s)", npy_filename.c_str(), e.what());
+    }
+}
+
 /** Load the tensor with pre-trained data from a binary file
  *
  * @param[in] tensor   The tensor to be filled. Data type supported: F32.
@@ -484,56 +779,51 @@ void load_trained_data(T &tensor, const std::string &filename)
     }
 }
 
-/** Obtain numpy type string from DataType.
- *
- * @param[in] data_type Data type.
- *
- * @return numpy type string.
- */
-inline std::string get_typestring(DataType data_type)
+template <typename T>
+void fill_random_tensor(T &tensor, float lower_bound, float upper_bound)
 {
-    // Check endianness
-    const unsigned int i = 1;
-    const char        *c = reinterpret_cast<const char *>(&i);
-    std::string        endianness;
-    if(*c == 1)
-    {
-        endianness = std::string("<");
-    }
-    else
-    {
-        endianness = std::string(">");
-    }
-    const std::string no_endianness("|");
+    std::random_device rd;
+    std::mt19937       gen(rd());
 
-    switch(data_type)
+    TensorShape shape(tensor.info()->dimension(0), tensor.info()->dimension(1));
+
+    Window window;
+    window.set(Window::DimX, Window::Dimension(0, shape.x(), 1));
+    window.set(Window::DimY, Window::Dimension(0, shape.y(), 1));
+
+    map(tensor, true);
+
+    Iterator it(&tensor, window);
+
+    switch(tensor.info()->format())
     {
-        case DataType::U8:
-            return no_endianness + "u" + support::cpp11::to_string(sizeof(uint8_t));
-        case DataType::S8:
-            return no_endianness + "i" + support::cpp11::to_string(sizeof(int8_t));
-        case DataType::U16:
-            return endianness + "u" + support::cpp11::to_string(sizeof(uint16_t));
-        case DataType::S16:
-            return endianness + "i" + support::cpp11::to_string(sizeof(int16_t));
-        case DataType::U32:
-            return endianness + "u" + support::cpp11::to_string(sizeof(uint32_t));
-        case DataType::S32:
-            return endianness + "i" + support::cpp11::to_string(sizeof(int32_t));
-        case DataType::U64:
-            return endianness + "u" + support::cpp11::to_string(sizeof(uint64_t));
-        case DataType::S64:
-            return endianness + "i" + support::cpp11::to_string(sizeof(int64_t));
-        case DataType::F32:
-            return endianness + "f" + support::cpp11::to_string(sizeof(float));
-        case DataType::F64:
-            return endianness + "f" + support::cpp11::to_string(sizeof(double));
-        case DataType::SIZET:
-            return endianness + "u" + support::cpp11::to_string(sizeof(size_t));
+        case arm_compute::Format::F32:
+        {
+            std::uniform_real_distribution<float> dist(lower_bound, upper_bound);
+
+            execute_window_loop(window, [&](const Coordinates & id)
+            {
+                *reinterpret_cast<float *>(it.ptr()) = dist(gen);
+            },
+            it);
+
+            break;
+        }
         default:
-            ARM_COMPUTE_ERROR("NOT SUPPORTED!");
+        {
+            ARM_COMPUTE_ERROR("Unsupported format");
+        }
     }
+
+    unmap(tensor);
+}
+
+template <typename T>
+void init_sgemm_output(T &dst, T &src0, T &src1, arm_compute::Format format)
+{
+    dst.allocator()->init(TensorInfo(src1.info()->dimension(0), src0.info()->dimension(1), format));
 }
+
 } // namespace utils
 } // namespace arm_compute
 #endif /* __UTILS_UTILS_H__*/