COMPMID-801: NHWC support in CLIm2Col.
And extended tests coverage adding kernel shapes 3x1, 1x5 and 7x7
Change-Id: Ia7c1d4da2368d5f5fbc1a41187f4ac1aca5f150f
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/127727
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp
index 009d4db..207efa6 100644
--- a/src/core/CL/CLKernelLibrary.cpp
+++ b/src/core/CL/CLKernelLibrary.cpp
@@ -272,6 +272,8 @@
{ "im2col_generic_dchw", "im2col.cl" },
{ "im2col_generic_padx0_pady0_dchw", "im2col.cl" },
{ "im2col_reduced_dchw", "im2col.cl" },
+ { "im2col3x3_nhwc", "im2col.cl" },
+ { "im2col_generic_nhwc", "im2col.cl" },
{ "init_level", "optical_flow_pyramid_lk.cl" },
{ "init_level_max", "optical_flow_pyramid_lk.cl" },
{ "init_level_max_initial_estimate", "optical_flow_pyramid_lk.cl" },
diff --git a/src/core/CL/cl_kernels/im2col.cl b/src/core/CL/cl_kernels/im2col.cl
index 1e85e1b..f53ce21 100644
--- a/src/core/CL/cl_kernels/im2col.cl
+++ b/src/core/CL/cl_kernels/im2col.cl
@@ -123,7 +123,207 @@
}
#endif // defined(CONVOLVED_WIDTH) && defined(STRIDE_Y) && defined(KERNEL_DEPTH)
+#define PTR_TO_VALUE(PTR, DATA_TYPE) *((DATA_TYPE *)(PTR))
+
#if defined(CONVOLVED_WIDTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(KERNEL_DEPTH) && defined(PAD_LEFT) && defined(PAD_RIGHT) && defined(PAD_TOP) && defined(PAD_BOTTOM) && defined(PAD_VALUE)
+
+/** This kernel performs a reshaping of the input tensor to a tensor used to perform convolution using GEMM when the kernel size is 5x5
+ *
+ * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
+ * @note The width and height of the input tensor must be passed at compile time using -DSRC_WIDTH and -DSRC_HEIGHT: e.g. -DSRC_WIDTH=128 and -DSRC_HEIGHT=128
+ * @note The width of output tensor after matrix multiplication must be passed at compile time using -DCONVOLVED_WIDTH: e.g. -DCONVOLVED_WIDTH=34
+ * @note The kernel depth must be passed at compile time using -DKERNEL_DEPTH: e.g. -DKERNEL_DEPTH=3
+ * @note The pad_left, pad_right, pad_top and pad_bottom must be passed at compile time using -DPAD_LEFT, -DPAD_RIGHT, -DPAD_TOP and -DPAD_BOTTOM: e.g. -DPAD_LEFT=1, -DPAD_RIGHT=2, -DPAD_TOP=3 and -DPAD_BOTTOM=2
+ * @note The zero value to store in case we load values out-of-bounds must be passed at compile time using -DPAD_VALUE: e.g. -DPAD_VALUE=0.0
+ * @note The stride along the X and Y directions must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y: e.g. -DSTRIDE_X=1 and -DSTRIDE_Y=1
+ * @note In case biases will be added to the convolution -DHAS_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: QS8/QASYMM8/QS16/F16/F32
+ * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
+ * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
+ * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor
+ * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr
+ * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)
+ * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ * @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).
+ */
+__kernel void im2col_generic_nhwc(
+ TENSOR3D_DECLARATION(src),
+ IMAGE_DECLARATION(dst),
+ uint src_stride_w,
+ uint dst_stride_w)
+{
+ const int src_stride_y_int = (int)src_stride_y;
+ const int src_stride_z_int = (int)src_stride_z;
+ const int xc = get_global_id(1); // x coordinate in the convolved tensor
+ const int yc = get_global_id(2) % CONVOLVED_HEIGHT; // y coordinate in the convolved tensor
+ const int ch = get_global_id(0); // input feature map
+ const int batch = get_global_id(2) / CONVOLVED_HEIGHT; // batch size
+
+ // Calculate input indices
+ const int xi = xc * STRIDE_X - PAD_LEFT;
+ const int yi = yc * STRIDE_Y - PAD_TOP;
+
+ // Calculate output indices
+ const int xo = ch * KERNEL_HEIGHT * KERNEL_WIDTH;
+ const int yo = xc + yc * CONVOLVED_WIDTH; // Index of the convolution
+
+ // Get input and output address
+ __global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + xi * src_stride_y_int + yi * src_stride_z_int + ch * src_stride_x + batch * src_stride_w;
+ __global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + xo * dst_stride_x + yo * dst_stride_y + batch * dst_stride_w;
+
+ for(int yk = 0; yk < KERNEL_HEIGHT; ++yk)
+ {
+ const int y0 = yi + yk;
+ if(y0 >= 0 && y0 < SRC_HEIGHT)
+ {
+ int xk;
+ for(xk = 0; xk < KERNEL_WIDTH; xk++)
+ {
+ const int x0 = xi + xk;
+ if(x0 >= 0 && x0 < SRC_WIDTH)
+ {
+ *((__global DATA_TYPE *)output_ptr) = PTR_TO_VALUE(input_ptr + xk * src_stride_y + yk * src_stride_z, DATA_TYPE);
+ }
+ else
+ {
+ *((__global DATA_TYPE *)output_ptr) = PAD_VALUE;
+ }
+ output_ptr += 1 * sizeof(DATA_TYPE);
+ }
+ }
+ else
+ {
+ for(int xk = 0; xk < KERNEL_WIDTH; xk++)
+ {
+ *((__global DATA_TYPE *)output_ptr) = (DATA_TYPE)PAD_VALUE;
+ output_ptr += 1 * dst_stride_x;
+ }
+ }
+ }
+#ifdef HAS_BIAS
+ if(ch == (KERNEL_DEPTH - 1))
+ {
+ *((__global DATA_TYPE *)output_ptr) = 1.0f;
+ output_ptr += 1 * dst_stride_x;
+ }
+#endif // HAS_BIAS
+}
+
+/** This kernel performs a reshaping of the input tensor (with layout NHWC) to a tensor used to perform convolution using GEMM when the kernel size is 3x3
+ *
+ * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
+ * @note The width and height of the input tensor must be passed at compile time using -DSRC_WIDTH and -DSRC_HEIGHT: e.g. -DSRC_WIDTH=128 and -DSRC_HEIGHT=128
+ * @note The width of output tensor after matrix multiplication must be passed at compile time using -DCONVOLVED_WIDTH: e.g. -DCONVOLVED_WIDTH=34
+ * @note The kernel depth must be passed at compile time using -DKERNEL_DEPTH: e.g. -DKERNEL_DEPTH=3
+ * @note The pad_left, pad_right, pad_top and pad_bottom must be passed at compile time using -DPAD_LEFT, -DPAD_RIGHT, -DPAD_TOP and -DPAD_BOTTOM: e.g. -DPAD_LEFT=1, -DPAD_RIGHT=2, -DPAD_TOP=3 and -DPAD_BOTTOM=2
+ * @note The zero value to store in case we load values out-of-bounds must be passed at compile time using -DPAD_VALUE: e.g. -DPAD_VALUE=0.0
+ * @note The stride along the X and Y directions must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y: e.g. -DSTRIDE_X=1 and -DSTRIDE_Y=1
+ * @note In case biases will be added to the convolution -DHAS_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: QS8/QASYMM8/QS16/F16/F32
+ * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
+ * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
+ * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor
+ * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr
+ * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)
+ * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ * @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).
+ */
+__kernel void im2col3x3_nhwc(
+ TENSOR3D_DECLARATION(src),
+ IMAGE_DECLARATION(dst),
+ uint src_stride_w,
+ uint dst_stride_w)
+{
+ const int src_stride_y_int = (int)src_stride_y;
+ const int src_stride_z_int = (int)src_stride_z;
+ const int xc = get_global_id(1); // x coordinate in the convolved tensor
+ const int yc = get_global_id(2) % CONVOLVED_HEIGHT; // y coordinate in the convolved tensor
+ const int ch = get_global_id(0); // input feature map
+ const int batch = get_global_id(2) / CONVOLVED_HEIGHT; // batch size
+
+ // Calculate input indices
+ const int xi = xc * STRIDE_X - PAD_LEFT;
+ const int yi = yc * STRIDE_Y - PAD_TOP;
+
+ // Calculate output indices
+ const int xo = ch * 9; // 3x3
+ const int yo = xc + yc * CONVOLVED_WIDTH; // Index of the convolution
+
+ // Get input and output address
+ __global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + xi * src_stride_y_int + yi * src_stride_z_int + ch * src_stride_x + batch * src_stride_w;
+ __global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + xo * dst_stride_x + yo * dst_stride_y + batch * dst_stride_w;
+
+ VEC_DATA_TYPE(DATA_TYPE, 3)
+ row0 = (VEC_DATA_TYPE(DATA_TYPE, 3))(PAD_VALUE);
+ VEC_DATA_TYPE(DATA_TYPE, 3)
+ row1 = (VEC_DATA_TYPE(DATA_TYPE, 3))(PAD_VALUE);
+ VEC_DATA_TYPE(DATA_TYPE, 3)
+ row2 = (VEC_DATA_TYPE(DATA_TYPE, 3))(PAD_VALUE);
+
+ const int3 y = (int3)yi + (int3)(0, 1, 2);
+ // Guard against reading outside the input buffer, there is no padding in Z so we check if ry is inside the buffer.
+ if(y.s0 >= 0 && y.s0 < SRC_HEIGHT)
+ {
+ row0 = (VEC_DATA_TYPE(DATA_TYPE, 3))(
+ PTR_TO_VALUE(input_ptr + 0 * src_stride_y, DATA_TYPE),
+ PTR_TO_VALUE(input_ptr + 1 * src_stride_y, DATA_TYPE),
+ PTR_TO_VALUE(input_ptr + 2 * src_stride_y, DATA_TYPE));
+ }
+
+ if(y.s1 >= 0 && y.s1 < SRC_HEIGHT)
+ {
+ row1 = (VEC_DATA_TYPE(DATA_TYPE, 3))(
+ PTR_TO_VALUE(input_ptr + 0 * src_stride_y + 1 * src_stride_z, DATA_TYPE),
+ PTR_TO_VALUE(input_ptr + 1 * src_stride_y + 1 * src_stride_z, DATA_TYPE),
+ PTR_TO_VALUE(input_ptr + 2 * src_stride_y + 1 * src_stride_z, DATA_TYPE));
+ }
+
+ if(y.s2 >= 0 && y.s2 < SRC_HEIGHT)
+ {
+ row2 = (VEC_DATA_TYPE(DATA_TYPE, 3))(
+ PTR_TO_VALUE(input_ptr + 0 * src_stride_y + 2 * src_stride_z, DATA_TYPE),
+ PTR_TO_VALUE(input_ptr + 1 * src_stride_y + 2 * src_stride_z, DATA_TYPE),
+ PTR_TO_VALUE(input_ptr + 2 * src_stride_y + 2 * src_stride_z, DATA_TYPE));
+ }
+
+#if PAD_LEFT != 0 || PAD_TOP != 0 || PAD_RIGHT != 0 || PAD_BOTTOM != 0
+ // Put 0 if the value is out-of-bound
+ const int3 x = (int3)xi + (int3)(0, 1, 2);
+ VEC_DATA_TYPE(COND_DATA_TYPE, 3)
+ cond0 = CONVERT((x >= (int3)0 && x < (int3)SRC_WIDTH), VEC_DATA_TYPE(COND_DATA_TYPE, 3));
+ row0 = select((VEC_DATA_TYPE(DATA_TYPE, 3))PAD_VALUE, row0, cond0);
+ row1 = select((VEC_DATA_TYPE(DATA_TYPE, 3))PAD_VALUE, row1, cond0);
+ row2 = select((VEC_DATA_TYPE(DATA_TYPE, 3))PAD_VALUE, row2, cond0);
+#endif // PAD_LEFT != 0 || PAD_TOP != 0 || PAD_RIGHT != 0 || PAD_BOTTOM != 0
+ vstore8((VEC_DATA_TYPE(DATA_TYPE, 8))(row0.s012, row1.s012, row2.s01), 0, (__global DATA_TYPE *)output_ptr);
+ *((__global DATA_TYPE *)output_ptr + 8) = row2.s2;
+
+#ifdef HAS_BIAS
+ if(ch == (KERNEL_DEPTH - 1))
+ {
+ *((__global DATA_TYPE *)output_ptr + 9) = 1.0f;
+ }
+#endif // HAS_BIAS
+}
+
/** This kernel performs a reshaping of the input tensor to a tensor used to perform convolution using GEMM when the kernel size is 3x3
*
* @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
@@ -804,4 +1004,4 @@
}
#endif // HAS_BIAS
}
-#endif // defined(DATA_TYPE) && defined(ELEMENT_SIZE)
\ No newline at end of file
+#endif // defined(DATA_TYPE) && defined(ELEMENT_SIZE)
diff --git a/src/core/CL/kernels/CLIm2ColKernel.cpp b/src/core/CL/kernels/CLIm2ColKernel.cpp
index 53a4dca..00d9fcb 100644
--- a/src/core/CL/kernels/CLIm2ColKernel.cpp
+++ b/src/core/CL/kernels/CLIm2ColKernel.cpp
@@ -31,7 +31,10 @@
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/Size2D.h"
+#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "support/ToolchainSupport.h"
#include <cmath>
@@ -48,6 +51,7 @@
ARM_COMPUTE_RETURN_ERROR_ON(input->data_type() == DataType::QASYMM8 && has_bias);
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output);
ARM_COMPUTE_RETURN_ERROR_ON((dilation.x() < 1) || (dilation.y() < 1));
+ ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() == DataLayout::UNKNOWN);
// Checks performed when output is configured
if(output->total_size() != 0)
@@ -58,6 +62,21 @@
return Status{};
}
+
+inline bool run_im2col_reduced(ITensorInfo *input, ITensorInfo *output, const PadStrideInfo &conv_info)
+{
+ int stride_x = 0;
+ int stride_y = 0;
+
+ std::tie(stride_x, stride_y) = conv_info.stride();
+
+ return (output->dimension(0) == (input->dimension(0) * input->dimension(1) * input->dimension(2))) && (TensorShape::num_max_dimensions >= 4)
+ && (std::equal(input->tensor_shape().cbegin() + 3,
+ input->tensor_shape().cend(),
+ output->tensor_shape().cbegin() + 1))
+ && ((stride_x == 1) && (stride_y == 1) && !conv_info.has_padding());
+}
+
} // namespace
CLIm2ColKernel::CLIm2ColKernel()
@@ -65,56 +84,41 @@
{
}
-void CLIm2ColKernel::configure(const ICLTensor *input, ICLTensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation)
+std::string
+CLIm2ColKernel::configure_window(const ICLTensor *input, ICLTensor *output, const Size2D &kernel_dims,
+ const Size2D &dilation, const PadStrideInfo &conv_info, CLBuildOptions &build_opts)
{
- ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
+ std::string kernel_name;
+ bool is_optimized_path = false;
+ const bool reduced = run_im2col_reduced(input->info(), output->info(), conv_info);
+ const DataType data_type = input->info()->data_type();
+ const bool squared_im2col = kernel_dims.width == kernel_dims.height;
+ const DataLayout data_layout = input->info()->data_layout();
- // Perform validation step
- ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), has_bias, dilation));
-
- _input = input;
- _output = output;
- _conv_info = conv_info;
- _kernel_dims = kernel_dims;
-
- const DataType data_type = input->info()->data_type();
- const GPUTarget gpu_target = get_target();
-
- // Create kernel
- CLBuildOptions build_opts;
- build_opts.add_option(("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type)));
- build_opts.add_option("-DELEMENT_SIZE=" + support::cpp11::to_string(input->info()->element_size()));
- build_opts.add_option_if(has_bias, "-DHAS_BIAS");
- build_opts.add_option_if(is_data_type_fixed_point(data_type), "-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position()));
-
- int stride_x = 0;
- int stride_y = 0;
-
- std::tie(stride_x, stride_y) = conv_info.stride();
-
- const bool run_img2col_reduced = (output->info()->dimension(0) == (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2))) && (TensorShape::num_max_dimensions >= 4)
- && (std::equal(input->info()->tensor_shape().cbegin() + 3,
- input->info()->tensor_shape().cend(),
- output->info()->tensor_shape().cbegin() + 1))
- && ((stride_x == 1) && (stride_y == 1) && !conv_info.has_padding());
-
- bool is_optimized_path = false;
-
- _num_elems_processed_per_iteration = 1;
-
- std::string kernel_name;
- if(!run_img2col_reduced)
+ if(!reduced)
{
// Default kernel name
- kernel_name = "im2col_generic_dchw";
+ if(data_layout == DataLayout::NCHW)
+ {
+ kernel_name = "im2col_generic_dchw";
+ }
+ else
+ {
+ kernel_name = "im2col_generic_nhwc";
+ }
- _convolved_dims = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1),
- kernel_dims.width, kernel_dims.height,
- conv_info, dilation);
+ const unsigned int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+ const unsigned int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+ const unsigned int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);
+ const unsigned int input_width = input->info()->dimension(width_idx);
+ const unsigned int input_height = input->info()->dimension(height_idx);
+ const unsigned int input_channel = input->info()->dimension(channel_idx);
+
+ _convolved_dims = scaled_dimensions(input_width, input_height, kernel_dims.width, kernel_dims.height, conv_info, dilation);
build_opts.add_option("-DKERNEL_WIDTH=" + support::cpp11::to_string(kernel_dims.width));
build_opts.add_option("-DKERNEL_HEIGHT=" + support::cpp11::to_string(kernel_dims.height));
- build_opts.add_option("-DKERNEL_DEPTH=" + support::cpp11::to_string(input->info()->dimension(2)));
+ build_opts.add_option("-DKERNEL_DEPTH=" + support::cpp11::to_string(input_channel));
build_opts.add_option("-DCONVOLVED_WIDTH=" + support::cpp11::to_string(_convolved_dims.first));
build_opts.add_option("-DCONVOLVED_HEIGHT=" + support::cpp11::to_string(_convolved_dims.second));
build_opts.add_option("-DSTRIDE_X=" + support::cpp11::to_string(conv_info.stride().first));
@@ -123,14 +127,12 @@
build_opts.add_option("-DPAD_TOP=" + support::cpp11::to_string(conv_info.pad_top()));
build_opts.add_option("-DPAD_RIGHT=" + support::cpp11::to_string(conv_info.pad_right()));
build_opts.add_option("-DPAD_BOTTOM=" + support::cpp11::to_string(conv_info.pad_bottom()));
- build_opts.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(input->info()->dimension(0)));
- build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(input->info()->dimension(1)));
+ build_opts.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(input_width));
+ build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(input_height));
build_opts.add_option("-DDILATION_X=" + support::cpp11::to_string(dilation.x()));
build_opts.add_option("-DDILATION_Y=" + support::cpp11::to_string(dilation.y()));
build_opts.add_option_if_else(is_data_type_quantized(data_type), "-DPAD_VALUE=" + support::cpp11::to_string(input->info()->quantization_info().offset), "-DPAD_VALUE=0");
- const bool squared_im2col = kernel_dims.width == kernel_dims.height;
-
if(dilation == Size2D(1U, 1U))
{
if(squared_im2col && !is_data_type_fixed_point(data_type))
@@ -153,12 +155,31 @@
_lws_hint = cl::NDRange(1, 1, 8);
_num_elems_processed_per_iteration = 1;
is_optimized_path = true;
- kernel_name = "im2col3x3_dchw";
+ switch(data_layout)
+ {
+ case DataLayout::NCHW:
+ kernel_name = "im2col3x3_dchw";
+ break;
+ case DataLayout::NHWC:
+ kernel_name = "im2col3x3_nhwc";
+ break;
+ default:
+ ARM_COMPUTE_ERROR("Not supported.");
+ break;
+ }
break;
case 5:
_num_elems_processed_per_iteration = 1;
is_optimized_path = true;
- kernel_name = "im2col5x5_dchw";
+ switch(data_layout)
+ {
+ case DataLayout::NCHW:
+ kernel_name = "im2col5x5_dchw";
+ break;
+ default:
+ // using generic_nhwc
+ break;
+ }
break;
case 11:
// Optimized im2col11x11 if pad_x = pad_y = 0
@@ -177,28 +198,34 @@
else if(kernel_dims.width > 1 && !conv_info.has_padding())
{
_num_elems_processed_per_iteration = 1;
- kernel_name = "im2col_generic_padx0_pady0_dchw";
+ is_optimized_path = false;
- // Optimized im2col is performed using one or more vector operations with the specified vector size
- // and a remainder. For example, for 5x5 convolutions, im2col is performed using vectors of size 4
- // and scalars; for 7x7 convolutions, using vectors of size 4 and vectors of size 3.
- // Using the vector size of 4 is always safe since OpenCL supports vectors of size 2 and 3.
- // Using the vector size of 8, however, may be faster.
- size_t vector_size = 4;
- // For 2x2 convolutions, use vectors of size 2. (For 3x3 convolutions, im2col_kernel3x3_padx0_pady0
- // is used instead.)
- if(kernel_dims.width < vector_size)
+ if(data_layout == DataLayout::NCHW)
{
- vector_size = kernel_dims.width;
+ kernel_name = "im2col_generic_padx0_pady0_dchw";
+
+ // Optimized im2col is performed using one or more vector operations with the specified vector size
+ // and a remainder. For example, for 5x5 convolutions, im2col is performed using vectors of size 4
+ // and scalars; for 7x7 convolutions, using vectors of size 4 and vectors of size 3.
+ // Using the vector size of 4 is always safe since OpenCL supports vectors of size 2 and 3.
+ // Using the vector size of 8, however, may be faster.
+ size_t vector_size = 4;
+ // For 2x2 convolutions, use vectors of size 2. (For 3x3 convolutions, im2col_kernel3x3_padx0_pady0
+ // is used instead.)
+ if(kernel_dims.width < vector_size)
+ {
+ vector_size = kernel_dims.width;
+ }
+ // Vector size optimized for the 11x11 AlexNet convolution on Bifrost.
+ const GPUTarget gpu_target = get_target();
+ if(gpu_target_is_in(gpu_target, GPUTarget::G71, GPUTarget::G72, GPUTarget::G51, GPUTarget::G51BIG, GPUTarget::G51LIT, GPUTarget::TNOX) && kernel_dims.width == 11)
+ {
+ vector_size = 8;
+ }
+ const size_t width_mod_vector_size = kernel_dims.width % vector_size;
+ build_opts.add_option("-DVECTOR_SIZE=" + support::cpp11::to_string(vector_size));
+ build_opts.add_option("-DWIDTH_MOD_VECTOR_SIZE=" + support::cpp11::to_string(width_mod_vector_size));
}
- // Vector size optimized for the 11x11 AlexNet convolution on Bifrost.
- if(gpu_target_is_in(gpu_target, GPUTarget::G71, GPUTarget::G72, GPUTarget::G51, GPUTarget::G51BIG, GPUTarget::G51LIT, GPUTarget::TNOX) && kernel_dims.width == 11)
- {
- vector_size = 8;
- }
- const size_t width_mod_vector_size = kernel_dims.width % vector_size;
- build_opts.add_option("-DVECTOR_SIZE=" + support::cpp11::to_string(vector_size));
- build_opts.add_option("-DWIDTH_MOD_VECTOR_SIZE=" + support::cpp11::to_string(width_mod_vector_size));
}
}
_run_func = &CLIm2ColKernel::run_generic;
@@ -209,27 +236,37 @@
kernel_name = "im2col_reduced_dchw";
_run_func = &CLIm2ColKernel::run_reduced;
}
-
- // Create kernel
- _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
-
// Configure kernel window
Window win;
if(is_optimized_path)
{
- win = calculate_max_window(*input->info(),
- Steps(_num_elems_processed_per_iteration),
- false,
- BorderSize(conv_info.pad_top(), conv_info.pad_right(), conv_info.pad_bottom(), conv_info.pad_left()));
+ if(data_layout == DataLayout::NHWC)
+ {
+ win = calculate_max_window(*input->info(),
+ Steps(_num_elems_processed_per_iteration),
+ false,
+ BorderSize(conv_info.pad_top(), conv_info.pad_right(), conv_info.pad_bottom(), conv_info.pad_left()));
+ const int x = -conv_info.pad_left();
+ const int y = -conv_info.pad_top();
+ const int h = kernel_dims.width * _num_elems_processed_per_iteration;
+ const int w = 1;
+ AccessWindowRectangle input_access(input->info(), x, y, w, h);
+ update_window_and_padding(win, input_access);
+ }
+ else
+ {
+ win = calculate_max_window(*input->info(),
+ Steps(_num_elems_processed_per_iteration),
+ false,
+ BorderSize(conv_info.pad_top(), conv_info.pad_right(), conv_info.pad_bottom(), conv_info.pad_left()));
- const int x = -conv_info.pad_left();
- const int y = -conv_info.pad_top();
- const int w = kernel_dims.width * _num_elems_processed_per_iteration;
- const int h = kernel_dims.height;
-
- AccessWindowRectangle input_access(input->info(), x, y, w, h);
-
- update_window_and_padding(win, input_access);
+ const int x = -conv_info.pad_left();
+ const int y = -conv_info.pad_top();
+ const int w = kernel_dims.width * _num_elems_processed_per_iteration;
+ const int h = kernel_dims.height;
+ AccessWindowRectangle input_access(input->info(), x, y, w, h);
+ update_window_and_padding(win, input_access);
+ }
}
else
{
@@ -239,13 +276,41 @@
}
output->info()->set_valid_region(ValidRegion(Coordinates(), output->info()->tensor_shape()));
- if(!run_img2col_reduced)
+ if(!reduced)
{
// set the Z dimension's step same size as the whole dimension so that one can't split across the Z dimension
win.set_dimension_step(Window::DimZ, win[Window::DimZ].end() - win[Window::DimZ].start());
}
-
ICLKernel::configure(win);
+ return kernel_name;
+}
+
+void CLIm2ColKernel::configure(const ICLTensor *input, ICLTensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation)
+{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
+ ARM_COMPUTE_ERROR_ON(input->info()->data_layout() == DataLayout::UNKNOWN);
+ ARM_COMPUTE_ERROR_ON_MSG(output->info()->data_layout() != DataLayout::NCHW, "Special case Im2Col output layout is NCHW");
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), has_bias, dilation));
+
+ _input = input;
+ _output = output;
+ _kernel_dims = kernel_dims;
+ _conv_info = conv_info;
+
+ const DataType data_type = input->info()->data_type();
+
+ // Create kernel
+ CLBuildOptions build_opts;
+ build_opts.add_option(("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type)));
+ build_opts.add_option("-DELEMENT_SIZE=" + support::cpp11::to_string(input->info()->element_size()));
+ build_opts.add_option_if(has_bias, "-DHAS_BIAS");
+ build_opts.add_option_if(is_data_type_fixed_point(data_type), "-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position()));
+
+ _num_elems_processed_per_iteration = 1;
+
+ const std::string kernel_name = configure_window(input, output, kernel_dims, dilation, conv_info, build_opts);
+ // Create kernel
+ _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
// Set config_id for enabling LWS tuning
_config_id = kernel_name;
@@ -277,23 +342,43 @@
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(ICLKernel::window(), window);
+ const DataLayout data_layout = _input->info()->data_layout();
+ const unsigned int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+ const unsigned int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+
// Get initial windows
Window window_collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ);
// Change the Z dimension's step back to 1
window_collapsed.set_dimension_step(Window::DimZ, 1);
- Window slice = window_collapsed.first_slice_window_3D();
- Window slice_in = window_collapsed.first_slice_window_3D();
- Window slice_out = window_collapsed.first_slice_window_3D();
+ const Window first_slice_3d = window_collapsed.first_slice_window_3D();
- // Setup slice if stride_x != 0 or stride_y != 0
- if(_convolved_dims.first != _input->info()->dimension(0) || _convolved_dims.second != _input->info()->dimension(1))
+ Window slice = first_slice_3d;
+ Window slice_in = first_slice_3d;
+ Window slice_out = first_slice_3d;
+
+ const bool out_dim_not_same_input_dim = _convolved_dims.first != _input->info()->dimension(width_idx) || _convolved_dims.second != _input->info()->dimension(height_idx);
+
+ // Setup slice if convolved dims are not the same as input dims
+ if(out_dim_not_same_input_dim)
{
// If the stride_x or stride_y are not 1, the output tensor of matrix multiply (Convolved tensor) will not
// have the same shape of the im2col input tensor
// In this case we need to re-compute the window using the shape of the tensor after matrix multiply (convolved_dims)
- slice.set(Window::DimX, Window::Dimension(0, static_cast<int>(_convolved_dims.first), 1));
- slice.set(Window::DimY, Window::Dimension(0, static_cast<int>(_convolved_dims.second), 1));
+ slice.set(width_idx, Window::Dimension(0, static_cast<int>(_convolved_dims.first), 1));
+ if(data_layout == DataLayout::NHWC)
+ {
+ // if layout is NHWC, we need to multiply convolved_dims.height by the number of batches as for this
+ // format we collapsed HEIGHT and all subsequent dimensions (batches) together. This is necessary to ensure
+ // global_id(2) values are in the correct range.
+ const Window tmp_win = window.collapse_if_possible(ICLKernel::window(), 3);
+ const int num_batches = tmp_win[3].end();
+ slice.set(height_idx, Window::Dimension(0, static_cast<int>(_convolved_dims.second) * num_batches, 1));
+ }
+ else
+ {
+ slice.set(height_idx, Window::Dimension(0, static_cast<int>(_convolved_dims.second), 1));
+ }
}
// Setup input slice
@@ -304,7 +389,7 @@
// Setup output slice
slice_out.set(Window::DimX, Window::Dimension(0, _output->info()->dimension(0), _kernel_dims.area()));
- slice_out.set(Window::DimY, Window::Dimension(0, _output->info()->dimension(1), 1));
+ slice_out.set(Window::DimY, Window::Dimension(0, _output->info()->dimension(1), _output->info()->dimension(1)));
slice_out.set(Window::DimZ, Window::Dimension(0, 1, 1));
do