APPBROWSER-377: GCConvoutionLayer support for FP16

Change-Id: I801b5e393a16a9f92c062826e6fcfd5982ca7bb3
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/116584
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

1 files changed, 472 insertions, 46 deletions

diff --git a/src/core/GLES_COMPUTE/cs_shaders/convolution_layer.cs b/src/core/GLES_COMPUTE/cs_shaders/convolution_layer.cs
index 4bfac282e2..2648db08b3 100755..100644
--- a/src/core/GLES_COMPUTE/cs_shaders/convolution_layer.cs
+++ b/src/core/GLES_COMPUTE/cs_shaders/convolution_layer.cs
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017, 2018 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -30,32 +30,163 @@ layout(local_size_x = LOCAL_SIZE_X, local_size_y = LOCAL_SIZE_Y, local_size_z =
 precision mediump float;
 #endif // DATA_TYPE_FP16
 
+#ifdef RESHAPE_TO_COLUMNS
+
+/** This kernel performs a reshaping of the input tensor to a tensor used to perform convolution using GEMM.
+ *
+ * @note The data type must be passed at compile time using "#define DATA_TYPE_NAME". e.g. "#define DATA_TYPE_FP32"
+ * @note In case biases will be added to the convolution "#define HAS_BIAS" has to be passed to append the final matrix with 1 in each row.
+ *
+ * @param[in]  src_ptr       Pointer to the source tensor. Supported data types: F16/F32
+ * @param[in]  src_attrs     The attributes of the source tensor
+ * @param[out] dst_ptr       Pointer to the destination tensor. Supported data types: same as @p src_ptr
+ * @param[in]  dst_attrs     The attributes of the destination tensor
+ * @param[in]  biases_ptr    Pointer to the biases tensor. Same as @p src_ptr
+ * @param[in]  biases_attrs  The attributes of the biases tensor
+ * @param[in]  width         The width of the input tensor
+ * @param[in]  height        The height of the input tensor
+ * @param[in]  depth         The depth of the input tensor
+ * @param[in]  total_filters Total number of filters. 4th dimension of the weights matrix
+ */
+
+SHADER_PARAMS_DECLARATION
+{
+    Tensor3DAttributes src_attrs;
+    ImageAttributes    dst_attrs;
+#ifdef HAS_BIAS
+    VectorAttributes biases_attrs;
+#endif /* HAS_BIAS */
+    uint width;
+    uint height;
+    uint depth;
+    uint total_filters;
+};
+
+#if defined(DATA_TYPE_FP16)
+
+TENSOR_DECLARATION(1, srcBuffer, uint, src_ptr, src_shift, 2, readonly);
+TENSOR_DECLARATION(2, dstBuffer, uint, dst_ptr, dst_shift, 2, writeonly);
+#ifdef HAS_BIAS
+TENSOR_DECLARATION(3, biasesBuffer, uint, biases_ptr, biases_shift, 2, readonly);
+#endif /* BIAS */
+
+void main()
+{
+    Tensor3DIterator src_iter = CONVERT_TO_TENSOR3D_ITERATOR(src_attrs, src_shift);
+    ImageIterator    dst_iter = CONVERT_TO_IMAGE_ITERATOR_NO_STEP(dst_attrs, dst_shift);
+#ifdef HAS_BIAS
+    VectorIterator biases_iter = CONVERT_TO_VECTOR_ITERATOR_NO_STEP(biases_attrs, biases_shift);
+#endif /* BIAS */
+
+    bool is_last_thread = (((int(gl_GlobalInvocationID.x)) == (int(gl_NumWorkGroups.x * gl_WorkGroupSize.x) - 1)) && ((int(gl_GlobalInvocationID.y)) == (int(gl_NumWorkGroups.y * gl_WorkGroupSize.y) - 1))
+                           && ((int(gl_GlobalInvocationID.z)) == (int(gl_NumWorkGroups.z * gl_WorkGroupSize.z) - 1)));
+    TENSOR_ITERATOR_ADVANCE_IN_BYTES(dst_iter, ((uint(gl_GlobalInvocationID.x) * uint(dst_attrs.stride_y)) + (uint(gl_GlobalInvocationID.y) * uint(width) * uint(dst_attrs.stride_y)) + (uint(
+                                                    gl_GlobalInvocationID.z)
+                                                * uint(width) * uint(height) * uint(dst_attrs.stride_y))));
+    // Linearize convolution elements
+    if(is_last_thread)
+    {
+        for(uint i = 0u; i < uint(total_filters); i = i + 2u)
+        {
+            vec2 s0 = LOAD_UNPACK2_CURRENT_ITEM_HALF(src_ptr, src_iter);
+            vec2 s;
+            if(int(CURRENT_ITEM_OFFSET_IN_BYTES(src_iter) >> 1u) % 2 == 0)
+            {
+                s.x = s0.x;
+            }
+            else
+            {
+                s.x = s0.y;
+            }
+            TENSOR_ITERATOR_ADVANCE_IN_BYTES(src_iter, (depth * src_attrs.stride_z));
+
+            vec2 s1 = LOAD_UNPACK2_CURRENT_ITEM_HALF(src_ptr, src_iter);
+            if(int(CURRENT_ITEM_OFFSET_IN_BYTES(src_iter) >> 1u) % 2 == 0)
+            {
+                s.y = s1.x;
+            }
+            else
+            {
+                s.y = s1.y;
+            }
+            STORE_PACK2_CURRENT_ITEM_HALF(dst_ptr, dst_iter, s);
+            TENSOR_ITERATOR_ADVANCE_IN_BYTES(src_iter, (depth * src_attrs.stride_z));
+#ifdef HAS_BIAS
+            vec2 b = LOAD_UNPACK2_CURRENT_ITEM_HALF(biases_ptr, biases_iter);
+            STORE_PACK2_HALF(dst_ptr, TENSOR_OFFSET_ADVANCE_IN_BYTES(dst_iter, dst_attrs.stride_y), b);
+            TENSOR_ITERATOR_ADVANCE_IN_BYTES(biases_iter, (2u * biases_attrs.stride_x));
+#endif /* HAS_BIAS */
+            TENSOR_ITERATOR_ADVANCE_IN_BYTES(dst_iter, (2u * dst_attrs.stride_x));
+        }
+    }
+    else
+    {
+        for(uint i = 0u; i < uint(total_filters); i = i + 2u)
+        {
+            vec2 s0 = LOAD_UNPACK2_CURRENT_ITEM_HALF(src_ptr, src_iter);
+            vec2 s;
+            if(int(CURRENT_ITEM_OFFSET_IN_BYTES(src_iter) >> 1u) % 2 == 0)
+            {
+                s.x = s0.x;
+            }
+            else
+            {
+                s.x = s0.y;
+            }
+            TENSOR_ITERATOR_ADVANCE_IN_BYTES(src_iter, (depth * src_attrs.stride_z));
+
+            vec2 s1 = LOAD_UNPACK2_CURRENT_ITEM_HALF(src_ptr, src_iter);
+            if(int(CURRENT_ITEM_OFFSET_IN_BYTES(src_iter) >> 1u) % 2 == 0)
+            {
+                s.y = s1.x;
+            }
+            else
+            {
+                s.y = s1.y;
+            }
+            STORE_PACK2_CURRENT_ITEM_HALF(dst_ptr, dst_iter, s);
+            TENSOR_ITERATOR_ADVANCE_IN_BYTES(src_iter, (depth * src_attrs.stride_z));
+            TENSOR_ITERATOR_ADVANCE_IN_BYTES(dst_iter, (2u * dst_attrs.stride_x));
+        }
+    }
+}
+
+#endif /* DATA_TYPE_FP16 */
+#endif // RESHAPE_TO_COLUMNS
+
 #ifdef IM2COL_GENERIC
+
 /** This kernel performs a reshaping of the input tensor to a tensor used to perform convolution using GEMM.
  *
  * @note The data type must be passed at compile time using "#define DATA_TYPE_FP32"
+ * @note PAD_LEFT/PAD_RIGHT/PAD_TOP/PAD_BOTTOM must be passed for padding info, e.g. "#define PAD_LEFT xxx"
+ * @note KERNEL_WIDTH/KERNEL_HEIGHT/KERNEL_DEPTH must be passed for kernel dimension, e.g. "#define KERNEL_WIDTH xxx"
+ * @note STRIDE_X/STRIDE_Y must be passed for stride info, e.g. "#define STRIDE_X xxx"
+ * @note CONVOLVED_WIDTH/CONVOLVED_HEIGHT must be passed for convolved dimension, e.g. "#define CONVOLVED_WIDTH xxx"
+ * @note SRC_WIDTH/SRC_HEIGHT must be passed for input dimension, e.g. "#define SRC_WIDTH xxx"
  * @note In case biases will be added to the convolution "#define HAS_BIAS" has to be passed to append the final matrix with 1 in each row.
  *
  * @param[in]  src_ptr      Pointer to the source tensor. Supported data types: F16/F32
  * @param[in]  src_attrs    The attributes of the source tensor
  * @param[out] dst_ptr      Pointer to the destination tensor. Supported data types: same as @p src_ptr
  * @param[in]  dst_attrs    The attributes of the destination tensor
- * @param[in]  filter_depth The depth of the used filter
  * @param[in]  src_stride_w Stride of the source tensor in W dimension (in bytes).
  * @param[in]  dst_stride_w Stride of the destination tensor in W dimension (in bytes).
  */
+
 SHADER_PARAMS_DECLARATION
 {
     Tensor3DAttributes src_attrs;
     ImageAttributes    dst_attrs;
-    uint               filter_depth;
     uint               src_stride_w;
     uint               dst_stride_w;
 };
 
 #ifdef DATA_TYPE_FP32
+
 TENSOR_DECLARATION(1, srcBuffer, float, src_ptr, src_shift, 2, readonly);
 TENSOR_DECLARATION(2, dstBuffer, float, dst_ptr, dst_shift, 2, restrict);
+
 void main(void)
 {
     Tensor3DIterator src_iter = CONVERT_TO_TENSOR3D_ITERATOR_NO_STEP(src_attrs, src_shift);
@@ -63,64 +194,315 @@ void main(void)
 
     uint xc    = gl_GlobalInvocationID.x;                // x coordinate in the convolved tensor
     uint yc    = gl_GlobalInvocationID.y;                // y coordinate in the convolved tensor
-    uint ch    = gl_GlobalInvocationID.z % filter_depth; // input feature map
-    uint batch = gl_GlobalInvocationID.z / filter_depth; // the batch
+    uint ch    = gl_GlobalInvocationID.z % KERNEL_DEPTH; // input feature map
+    uint batch = gl_GlobalInvocationID.z / KERNEL_DEPTH; // the batch
 
     // Calculate input indeces
-    uint xi           = xc * uint(STRIDE_X) - uint(PAD_X);
-    uint yi           = yc * uint(STRIDE_Y) - uint(PAD_Y);
-    uint input_offset = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, (ch * src_attrs.stride_z) + (batch * src_stride_w));
+    uint xi = xc * uint(STRIDE_X) - uint(PAD_LEFT);
+    uint yi = yc * uint(STRIDE_Y) - uint(PAD_TOP);
+    TENSOR_ITERATOR_ADVANCE_IN_BYTES(src_iter, (ch * src_attrs.stride_z) + (batch * src_stride_w));
 
     // Calculate output indeces
-    uint xo            = ch * uint(KERNEL_WIDTH) * uint(KERNEL_HEIGHT);
-    uint yo            = xc + yc * uint(CONVOLVED_WIDTH); // Index of the convolution
-    uint output_offset = TENSOR_OFFSET_ADVANCE_IN_BYTES(dst_iter, (yo * dst_attrs.stride_y) + (batch * dst_stride_w) + xo);
+    uint xo = ch * uint(KERNEL_WIDTH) * uint(KERNEL_HEIGHT);
+    uint yo = xc + yc * uint(CONVOLVED_WIDTH); // Index of the convolution
+    TENSOR_ITERATOR_ADVANCE_IN_BYTES(dst_iter, (yo * dst_attrs.stride_y) + (batch * dst_stride_w) + xo);
+
+    uint src_pos = 0u;
 
     // Linearize convolution elements
     for(uint y = yi, y_e = yi + uint(KERNEL_HEIGHT); y < y_e; ++y)
     {
-        for(uint x = xi, x_e = xi + uint(KERNEL_WIDTH); x < x_e; ++x)
+        for(uint x = xi, x_e = xi + uint(KERNEL_WIDTH); x < x_e; ++x, TENSOR_OFFSET_ADVANCE(dst_iter, 1u))
         {
-#if PAD_X == 0 && PAD_Y == 0
-            output_offset = input_offset + ((x * src_attrs.stride_x + y * src_attrs.stride_y) >> uint(2));
-            STORE(dst_ptr, output_offset, LOAD(src_ptr, input_offset));
-
-#else  // PAD_X == 0 && PAD_Y == 0
+#if PAD_LEFT == 0 && PAD_TOP == 0 && PAD_RIGHT == 0 && PAD_BOTTOM == 0
+            src_pos = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, x * src_attrs.stride_x + y * src_attrs.stride_y);
+            STORE_CURRENT_ITEM(dst_ptr, dst_iter, LOAD(src_ptr, src_pos));
+#else  /* PAD_LEFT == 0 && PAD_TOP == 0 && PAD_RIGHT == 0 && PAD_BOTTOM == 0 */
             if(x < 0 || x >= SRC_WIDTH || y < 0 || y >= SRC_HEIGHT)
             {
-                STORE(dst_ptr, output_offset, 0.0f);
+                STORE_CURRENT_ITEM(dst_ptr, dst_iter, 0.0f);
             }
             else
             {
-                output_offset = input_offset + (x * srcs_attrs.stride_x + y * src_attrs.stride_y) >> uint(2));
-                STORE(dst_ptr, output_offset, LOAD(src_ptr, input_offset));
+                src_pos = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, x * src_attrs.stride_x + y * src_attrs.stride_y);
+                STORE_CURRENT_ITEM(dst_ptr, dst_iter, LOAD(src_ptr, src_pos));
             }
-#endif // PAD_X == 0 && PAD_Y == 0
+#endif /* PAD_LEFT == 0 && PAD_TOP == 0 && PAD_RIGHT == 0 && PAD_BOTTOM == 0 */
         }
     }
 
 #ifdef HAS_BIAS
     if(ch == (uint(KERNEL_DEPTH) - 1))
     {
-        STORE(dst_ptr, output_offset, 1.0f);
+        STORE_CURRENT_ITEM(dst_ptr, dst_iter, 1.0f);
     }
-#endif // HAS_BIAS
+#endif /* HAS_BIAS */
 }
 
 #elif defined(DATA_TYPE_FP16)
+
 TENSOR_DECLARATION(1, srcBuffer, uint, src_ptr, src_shift, 2, readonly);
 TENSOR_DECLARATION(2, dstBuffer, uint, dst_ptr, dst_shift, 2, writeonly);
 
+#ifdef KERNEL_1x1
+
+void main(void)
+{
+    Tensor3DIterator src_iter = CONVERT_TO_TENSOR3D_ITERATOR_NO_STEP(src_attrs, src_shift);
+    ImageIterator    dst_iter = CONVERT_TO_IMAGE_ITERATOR_NO_STEP(dst_attrs, dst_shift);
+
+    uint xc    = gl_GlobalInvocationID.x;
+    uint yc    = gl_GlobalInvocationID.y;
+    uint zc    = gl_GlobalInvocationID.z;
+    uint ch    = zc % uint(KERNEL_DEPTH); // input feature map
+    uint batch = zc / uint(KERNEL_DEPTH); // the batch
+
+    // Calculate input indeces
+    uint xi = xc;
+    uint yi = yc;
+    TENSOR_ITERATOR_ADVANCE_IN_BYTES(src_iter, batch * src_stride_w + ch * src_attrs.step_z);
+
+    // Calculate output indeces
+    uint dst_element_count = dst_attrs.step_x / dst_attrs.stride_x;
+    uint xo                = ch * dst_element_count;
+    uint yo                = xc + yc * uint(CONVOLVED_WIDTH);
+    TENSOR_ITERATOR_ADVANCE_IN_BYTES(dst_iter, batch * dst_stride_w + yo * dst_attrs.stride_y + xo);
+
+    bool x_start_even = ((xc % 2u) == 0u);
+    bool z_depth_even = ((uint(KERNEL_DEPTH) % 2u) == 0u);
+    uint input_pos    = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, xi * src_attrs.stride_x + yi * src_attrs.stride_y);
+    uint tmp_left     = 0u;
+    uint tmp_right    = 0u;
+
+    if(ch % 2u != 0u)
+    {
+        return;
+    }
+
+    if(z_depth_even || (!z_depth_even && (int(ch) < (KERNEL_DEPTH - 1))))
+    {
+        tmp_left  = LOAD(src_ptr, input_pos);
+        input_pos = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, xi * src_attrs.stride_x + yi * src_attrs.stride_y + src_attrs.stride_z);
+        tmp_right = LOAD(src_ptr, input_pos);
+        if(x_start_even)
+        {
+            tmp_right = (tmp_left & 0xffffu) + (tmp_right << 16u);
+        }
+        else
+        {
+            tmp_right = (tmp_left >> 16u) + (tmp_right & 0xffff0000u);
+        }
+        STORE_CURRENT_ITEM(dst_ptr, dst_iter, tmp_right);
+        TENSOR_ITERATOR_ADVANCE_IN_BYTES(dst_iter, dst_attrs.step_x);
+
+#ifdef HAS_BIAS
+        if(ch == (uint(KERNEL_DEPTH) - 2u))
+        {
+            mediump vec2 bias_vec = vec2(1.f, 0.f);
+            uint         bias_u   = packHalf2x16(bias_vec);
+            STORE_CURRENT_ITEM(dst_ptr, dst_iter, bias_u);
+        }
+#endif /* HAS_BIAS */
+    }
+    else
+    {
+        tmp_left = LOAD(src_ptr, input_pos);
+        if(x_start_even)
+        {
+            tmp_right = (tmp_left & 0xffffu);
+        }
+        else
+        {
+            tmp_right = (tmp_left >> 16u);
+        }
+
+#ifdef HAS_BIAS
+        mediump vec2 bias_vec = vec2(0.f, 1.f);
+        uint         bias_u   = packHalf2x16(bias_vec);
+        tmp_right += (bias_u & 0xffff0000u);
+#endif /* HAS_BIAS */
+
+        STORE_CURRENT_ITEM(dst_ptr, dst_iter, tmp_right);
+    }
+}
+
+#else /* KERNEL_1x1 */
+
 void main(void)
 {
+    uint xc    = gl_GlobalInvocationID.x;
+    uint yc    = gl_GlobalInvocationID.y;
+    uint zc    = gl_GlobalInvocationID.z;
+    uint ch    = zc % uint(KERNEL_DEPTH); // input feature map
+    uint batch = zc / uint(KERNEL_DEPTH); // the batch
+
+    Tensor3DIterator src_iter   = CONVERT_TO_TENSOR3D_ITERATOR_NO_STEP(src_attrs, src_shift);
+    Tensor3DIterator src_iter_b = CONVERT_TO_TENSOR3D_ITERATOR_NO_STEP(src_attrs, src_shift);
+    ImageIterator    dst_iter   = CONVERT_TO_IMAGE_ITERATOR_NO_STEP(dst_attrs, dst_shift);
+
+    // Calculate input indeces
+    uint src_element_count = src_attrs.step_x / src_attrs.stride_x;
+    uint xi                = (xc * uint(STRIDE_X)) / src_element_count;
+    uint yi                = yc * uint(STRIDE_Y);
+    TENSOR_ITERATOR_ADVANCE_IN_BYTES(src_iter, batch * src_stride_w + ch * src_attrs.stride_z);
+
+    // Calculate output indeces
+    uint dst_element_count = dst_attrs.step_x / dst_attrs.stride_x;
+    uint xo                = (ch * uint(KERNEL_WIDTH) * uint(KERNEL_HEIGHT)) * dst_element_count;
+    uint yo                = xc + yc * uint(CONVOLVED_WIDTH);
+    TENSOR_ITERATOR_ADVANCE_IN_BYTES(dst_iter, batch * dst_stride_w + yo * dst_attrs.stride_y + xo);
+
+    bool x_start_even = ((xc * uint(STRIDE_X)) % 2u == 0u);
+    bool z_start_even = ((ch % 2u) == 0u);
+    uint input_pos    = 0u;
+    uint tmp          = 0u;
+    uint tmp_left     = 0u;
+    uint tmp_right    = 0u;
+
+    // Linearize convolution elements
+    for(uint y = yi, y_e = yi + uint(KERNEL_HEIGHT); y < y_e; ++y)
+    {
+        uint xstart = 0u;
+        uint xend   = 0u;
+
+        // even col, even row
+        if(x_start_even)
+        {
+            if(((y - yi + ch) % 2u) == 0u)
+            {
+                for(uint x = xi, x_e = xi + (uint(KERNEL_WIDTH) / 2u); x < x_e; ++x, TENSOR_ITERATOR_ADVANCE_IN_BYTES(dst_iter, dst_attrs.step_x))
+                {
+                    input_pos = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, x * src_attrs.step_x + y * src_attrs.stride_y);
+                    STORE_CURRENT_ITEM(dst_ptr, dst_iter, LOAD(src_ptr, input_pos));
+                }
+            }
+            else
+            {
+                // 1st pair
+                if(!z_start_even && (y == yi))
+                {
+                    // cross 2d feature map
+                    input_pos = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter_b, (xi + (uint(KERNEL_WIDTH) / 2u)) * src_attrs.step_x + (yi + uint(KERNEL_HEIGHT) - 1u) * src_attrs.stride_y + batch * src_stride_w +
+                                                               (ch - 1u) * src_attrs.stride_z);
+                }
+                else
+                {
+                    input_pos = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter,
+                                                               (xi + (uint(KERNEL_WIDTH) / 2u)) * src_attrs.step_x + (y - 1u) * src_attrs.stride_y);
+                }
+                tmp_right = LOAD(src_ptr, input_pos);
+                input_pos = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, xi * src_attrs.step_x + y * src_attrs.stride_y);
+                tmp_left  = LOAD(src_ptr, input_pos);
+                tmp_right = (tmp_right & 0xffffu) + (tmp_left << 16u);
+                STORE_CURRENT_ITEM(dst_ptr, dst_iter, tmp_right);
+                TENSOR_ITERATOR_ADVANCE_IN_BYTES(dst_iter, dst_attrs.step_x);
+
+                // remaining
+                for(uint x = xi + 1u, x_e = xi + (uint(KERNEL_WIDTH) / 2u) + 1u; x < x_e; ++x, TENSOR_ITERATOR_ADVANCE_IN_BYTES(dst_iter, dst_attrs.step_x))
+                {
+                    input_pos = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, (x - 1u) * src_attrs.step_x + y * src_attrs.stride_y);
+                    tmp_left  = LOAD(src_ptr, input_pos);
+                    input_pos = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, x * src_attrs.step_x + y * src_attrs.stride_y);
+                    tmp_right = LOAD(src_ptr, input_pos);
+                    tmp_right = (tmp_left >> 16u) + (tmp_right << 16u);
+                    STORE_CURRENT_ITEM(dst_ptr, dst_iter, tmp_right);
+                }
+            }
+        }
+        else
+        {
+            if((((y - yi) % 2u) == 0u && !z_start_even) || (((y - yi) % 2u) != 0u && z_start_even))
+            {
+                // 1st pair
+                if(y == yi)
+                {
+                    // cross 2d feature map
+                    input_pos = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter_b, (xi + (uint(KERNEL_WIDTH) / 2u)) * src_attrs.step_x + (yi + uint(KERNEL_HEIGHT) - 1u) * src_attrs.stride_y + batch * src_stride_w +
+                                                               (ch - 1u) * src_attrs.stride_z);
+                }
+                else
+                {
+                    input_pos = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter,
+                                                               (xi + (uint(KERNEL_WIDTH) / 2u)) * src_attrs.step_x + (y - 1u) * src_attrs.stride_y);
+                }
+
+                tmp_right = LOAD(src_ptr, input_pos);
+                input_pos = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, xi * src_attrs.step_x + y * src_attrs.stride_y);
+                tmp_left  = LOAD(src_ptr, input_pos);
+                tmp_right = (tmp_right >> 16u) + (tmp_left & 0xffff0000u);
+                STORE_CURRENT_ITEM(dst_ptr, dst_iter, tmp_right);
+                TENSOR_ITERATOR_ADVANCE_IN_BYTES(dst_iter, dst_attrs.step_x);
+
+                // remaining
+                for(uint x = xi + 1u, x_e = xi + (uint(KERNEL_WIDTH) / 2u) + 1u; x < x_e; ++x, TENSOR_ITERATOR_ADVANCE_IN_BYTES(dst_iter, dst_attrs.step_x))
+                {
+                    input_pos = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, x * src_attrs.step_x + y * src_attrs.stride_y);
+                    STORE_CURRENT_ITEM(dst_ptr, dst_iter, LOAD(src_ptr, input_pos));
+                }
+            }
+            else if((((y - yi) % 2u) == 0u && z_start_even) || (((y - yi) % 2u) != 0u && !z_start_even))
+            {
+                // 1st pair
+                input_pos = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, xi * src_attrs.step_x + y * src_attrs.stride_y);
+                tmp_right = LOAD(src_ptr, input_pos);
+                input_pos = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, (xi + 1u) * src_attrs.step_x + y * src_attrs.stride_y);
+                tmp_left  = LOAD(src_ptr, input_pos);
+                tmp_right = (tmp_right >> 16u) + (tmp_left << 16u);
+                STORE_CURRENT_ITEM(dst_ptr, dst_iter, tmp_right);
+                TENSOR_ITERATOR_ADVANCE_IN_BYTES(dst_iter, dst_attrs.step_x);
+
+                // remaining
+                for(uint x = xi + 1u, x_e = xi + (uint(KERNEL_WIDTH) / 2u); x < x_e; ++x, TENSOR_ITERATOR_ADVANCE_IN_BYTES(dst_iter, dst_attrs.step_x))
+                {
+                    input_pos = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, x * src_attrs.step_x + y * src_attrs.stride_y);
+                    tmp_right = LOAD(src_ptr, input_pos);
+                    input_pos = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, (x + 1u) * src_attrs.step_x + y * src_attrs.stride_y);
+                    tmp_left  = LOAD(src_ptr, input_pos);
+                    tmp_right = (tmp_right >> 16u) + (tmp_left << 16u);
+                    STORE_CURRENT_ITEM(dst_ptr, dst_iter, tmp_right);
+                }
+            }
+        }
+    }
+
+    // NOTE: must handle last element manually instead of in loops
+    // to avoid write conflict across 2d boundary
+    if(ch == uint(KERNEL_DEPTH) - 1u)
+    {
+        uint x    = xi + (uint(KERNEL_WIDTH) / 2u);
+        uint y    = yi + uint(KERNEL_HEIGHT) - 1u;
+        input_pos = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, x * src_attrs.step_x + y * src_attrs.stride_y);
+        tmp       = LOAD(src_ptr, input_pos);
+        if(!x_start_even)
+        {
+            tmp = (tmp >> 16u) + (tmp << 16u);
+        }
+
+#ifdef HAS_BIAS
+        mediump vec2 bias_vec = vec2(1.f, 1.f);
+        uint         bias_u   = packHalf2x16(bias_vec);
+        if(z_start_even)
+        {
+            tmp = (tmp & 0xffffu) + (bias_u & 0xffff0000u);
+        }
+        else
+        {
+            tmp = (bias_u & 0xffffu);
+        }
+#endif /* HAS_BIAS */
+
+        STORE_CURRENT_ITEM(dst_ptr, dst_iter, tmp);
+    }
 }
 
-#else /* DATA_TYPE_FP32 */
+#endif /* KERNEL_1x1 */
+#else  /* DATA_TYPE_FP32 */
 #error Data type not supported
 #endif /* DATA_TYPE_FP32 */
 #endif /* IM2COL_GENERIC */
 
 #ifdef IM2COL_REDUCED
+
 /** This kernel reshapes the tensor's low three dimensions to single row for GEMM operation
  *
  * @note The data type must be passed at compile time using "#define DATA_TYPE_FP16"
@@ -133,6 +515,7 @@ void main(void)
  * @param[in]  width     The width of the input tensor
  * @param[in]  height    The height of the input tensor
  */
+
 SHADER_PARAMS_DECLARATION
 {
     Tensor3DAttributes src_attrs;
@@ -142,6 +525,7 @@ SHADER_PARAMS_DECLARATION
 };
 
 #ifdef DATA_TYPE_FP32
+
 TENSOR_DECLARATION(1, srcBuffer, float, src_ptr, src_shift, 2, readonly);
 TENSOR_DECLARATION(2, dstBuffer, float, dst_ptr, dst_shift, 2, restrict);
 
@@ -181,6 +565,7 @@ TENSOR_DECLARATION(2, dstBuffer, uint, dst_ptr, dst_shift, 2, restrict);
 #endif                           /* IM2COL_REDUCED_8X */
 
 #if defined(IM2COL_REDUCED_GENERIC)
+
 void main(void)
 {
     Tensor3DIterator src_iter        = CONVERT_TO_TENSOR3D_ITERATOR(src_attrs, src_shift);
@@ -207,20 +592,20 @@ void main(void)
         else
         {
             // special op
-            uint tmpleft  = uint(0);
-            uint tmpright = uint(0);
-            tmpright      = LOAD_CURRENT_ITEM(src_ptr, src_iter); //right half
+            uint tmp_left  = uint(0);
+            uint tmp_right = uint(0);
+            tmp_right      = LOAD_CURRENT_ITEM(src_ptr, src_iter); //right half
             if(pos.x == uint(0))
             {
-                tmpleft  = LOAD(src_ptr, TENSOR3D_OFFSET(src_nostep_iter, int(width), int(pos.y) - 1, int(pos.z))); //left half
-                tmpright = (tmpleft & uint(0xffff)) + (tmpright << uint(16));
+                tmp_left  = LOAD(src_ptr, TENSOR3D_OFFSET(src_nostep_iter, int(width), int(pos.y) - 1, int(pos.z))); //left half
+                tmp_right = (tmp_left & uint(0xffff)) + (tmp_right << uint(16));
             }
             else
             {
-                tmpleft  = LOAD(src_ptr, TENSOR3D_OFFSET(src_nostep_iter, (int(pos.x) - 1) * int(element_count), int(pos.y), int(pos.z)));
-                tmpright = ((tmpleft >> uint(16)) + (tmpright << uint(16)));
+                tmp_left  = LOAD(src_ptr, TENSOR3D_OFFSET(src_nostep_iter, (int(pos.x) - 1) * int(element_count), int(pos.y), int(pos.z)));
+                tmp_right = ((tmp_left >> uint(16)) + (tmp_right << uint(16)));
             }
-            STORE(dst_ptr, tmp_out_offset, tmpright);
+            STORE(dst_ptr, tmp_out_offset, tmp_right);
         }
     }
     else
@@ -243,6 +628,7 @@ void main(void)
 }
 
 #else /* IM2COL_REDUCED_GENERIC */
+
 void main(void)
 {
     Tensor3DIterator src_iter = CONVERT_TO_TENSOR3D_ITERATOR(src_attrs, src_shift);
@@ -263,46 +649,86 @@ void main(void)
     STORE(dst_ptr, tmp_out_offset, tmp);
 #endif                           /* IM2COL_REDUCED_8X */
 }
-#endif                           /* IM2COL_REDUCED_GENERIC */
-#else                            /* DATA_TYPE_FP32 */
+
+#endif /* IM2COL_REDUCED_GENERIC */
+#else  /* DATA_TYPE_FP32 */
 #error Data type not supported
 #endif /* DATA_TYPE_FP32 */
 #endif /* IM2COL_REDUCED */
 
-#ifdef COL2IM
+#ifdef WIDTH_OUTPUT
+
 /** This kernel performs a reshaping of the output of the convolution layer.
  *
  * @note The data type must be passed at compile time using "#define DATA_TYPE_FP32"
  *
- * @param[in]  src_ptr   Pointer to the source tensor. Supported data types: F16/F32
- * @param[in]  src_attrs The attributes of the source tensor
- * @param[out] dst_ptr   Pointer to the destination tensor. Supported data types: same as @p src_ptr
- * @param[in]  dst_attrs The attributes of the destination tensor
- * @param[in]  width     The width of output convolved dimensions
+ * @param[in]  src_ptr     Pointer to the source tensor. Supported data types: F16/F32
+ * @param[in]  src_attrs   The attributes of the source tensor
+ * @param[out] dst_ptr     Pointer to the destination tensor. Supported data types: same as @p src_ptr
+ * @param[in]  dst_attrs   The attributes of the destination tensor
+ * @param[in]  dst_depth   The length of the destination tensor in Z dimension
+ * @param[in]  dst_strideZ The actual stride of the destination tensor in Z dimension
  */
+
 SHADER_PARAMS_DECLARATION
 {
-    ImageAttributes    src_attrs;
+    Tensor3DAttributes src_attrs;
     Tensor3DAttributes dst_attrs;
-    uint               width;
+    uint               dst_depth;
+    uint               dst_strideZ;
 };
 
 #ifdef DATA_TYPE_FP32
+
 TENSOR_DECLARATION(1, srcBuffer, float, src_ptr, src_shift, 2, readonly);
 TENSOR_DECLARATION(2, dstBuffer, float, dst_ptr, dst_shift, 2, restrict);
+
 void main(void)
 {
-    ImageIterator    src_iter = CONVERT_TO_IMAGE_ITERATOR(src_attrs, src_shift);
+    Tensor3DIterator src_iter = CONVERT_TO_TENSOR3D_ITERATOR_NO_STEP(src_attrs, src_shift);
     Tensor3DIterator dst_iter = CONVERT_TO_TENSOR3D_ITERATOR(dst_attrs, dst_shift);
 
-    uvec2 pos            = uvec2(gl_GlobalInvocationID.xy);
-    uint  tmp_out_offset = TENSOR3D_OFFSET(dst_iter, pos.y % width, pos.y / width, pos.x);
+    uvec3 pos = uvec3(gl_GlobalInvocationID.xyz);
+    TENSOR_ITERATOR_ADVANCE_IN_BYTES(src_iter, pos.x * src_attrs.step_y + pos.y * WIDTH_OUTPUT * src_attrs.step_y + (pos.z % dst_depth) * src_attrs.stride_x + (pos.z / dst_depth) * (src_attrs.stride_z));
 
-    STORE(dst_ptr, tmp_out_offset, LOAD_CURRENT_ITEM(src_ptr, src_iter));
+    STORE_CURRENT_ITEM(dst_ptr, dst_iter,
+                       LOAD_CURRENT_ITEM(src_ptr, src_iter));
 }
 
 #elif defined(DATA_TYPE_FP16)
 
+TENSOR_DECLARATION(1, srcBuffer, uint, src_ptr, src_shift, 2, readonly);
+TENSOR_DECLARATION(2, dstBuffer, uint, dst_ptr, dst_shift, 2, restrict);
+
+void main(void)
+{
+    Tensor3DIterator src_iter = CONVERT_TO_TENSOR3D_ITERATOR_NO_STEP(src_attrs, src_shift);
+    Tensor3DIterator dst_iter = CONVERT_TO_TENSOR3D_ITERATOR(dst_attrs, dst_shift);
+
+    uvec3 pos = uvec3(gl_GlobalInvocationID.xyz);
+
+    if((pos.z % dst_depth) % 2u == 0u)
+    {
+        uint common_offset_in_bytes = pos.x * src_attrs.step_y * 2u + pos.y * uint(WIDTH_OUTPUT) * src_attrs.step_y + (pos.z % dst_depth) * src_attrs.stride_x + (pos.z / dst_depth) * dst_strideZ;
+        uint tmp1_in_offset         = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, common_offset_in_bytes);
+        uint tmp2_in_offset         = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, common_offset_in_bytes + src_attrs.step_y);
+        vec2 tmp1                   = LOAD_UNPACK2_HALF(src_ptr, tmp1_in_offset);
+        vec2 tmp2                   = LOAD_UNPACK2_HALF(src_ptr, tmp2_in_offset);
+        vec2 result                 = vec2(tmp1.x, tmp2.x);
+        STORE_PACK2_CURRENT_ITEM_HALF(dst_ptr, dst_iter, result);
+    }
+    else
+    {
+        uint common_offset_in_bytes = pos.x * src_attrs.step_y * 2u + pos.y * uint(WIDTH_OUTPUT) * src_attrs.step_y + (pos.z % dst_depth) * src_attrs.stride_x + (pos.z / dst_depth) * dst_strideZ - 2u;
+        uint tmp1_in_offset         = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, common_offset_in_bytes);
+        uint tmp2_in_offset         = TENSOR_OFFSET_ADVANCE_IN_BYTES(src_iter, common_offset_in_bytes + src_attrs.step_y);
+        vec2 tmp1                   = LOAD_UNPACK2_HALF(src_ptr, tmp1_in_offset);
+        vec2 tmp2                   = LOAD_UNPACK2_HALF(src_ptr, tmp2_in_offset);
+        vec2 result                 = vec2(tmp1.y, tmp2.y);
+        STORE_PACK2_CURRENT_ITEM_HALF(dst_ptr, dst_iter, result);
+    }
+}
+
 #else /* DATA_TYPE_FP32 */
 #error Data type not supported
 #endif /* DATA_TYPE_FP32 */