COMPMID-1519: Add support for 3D input/output in CLGEMMLowpOutputStage
Change-Id: I637add70310d2da4d82b236a6352af9d33be17a1
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/149706
Reviewed-by: Isabella Gottardi <isabella.gottardi@arm.com>
Reviewed-by: Michele DiGiorgio <michele.digiorgio@arm.com>
Tested-by: bsgcomp <bsgcomp@arm.com>
diff --git a/src/core/CL/cl_kernels/gemmlowp.cl b/src/core/CL/cl_kernels/gemmlowp.cl
index e52f1ea..e8124e7 100644
--- a/src/core/CL/cl_kernels/gemmlowp.cl
+++ b/src/core/CL/cl_kernels/gemmlowp.cl
@@ -2222,17 +2222,29 @@
* @param[in] dst_step_y dst_gx_stride_y * number of elements along Y processed per workitem(in bytes)
* @param[in] dst_stride_z Stride of the source tensor in Z dimension (in bytes)
* @param[in] dst_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] dst_stride_w Stride of the source tensor in W dimension (in bytes)
+ * @param[in] dst_step_w src_stride_w * number of elements along W processed per workitem(in bytes)
* @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
*/
__kernel void gemmlowp_output_stage_quantize_down_fixedpoint(TENSOR3D_DECLARATION(src),
#if defined(ADD_BIAS)
VECTOR_DECLARATION(biases),
#endif // defined(ADD_BIAS)
+#if defined(DST_HEIGHT)
+ TENSOR4D_DECLARATION(dst))
+#else // defined(DST_HEIGHT)
TENSOR3D_DECLARATION(dst))
+#endif // defined(DST_HEIGHT)
{
// Compute source and destination addresses
Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src);
+#if defined(DST_HEIGHT)
+ Tensor4D dst = CONVERT_TO_TENSOR4D_STRUCT_NO_STEP(dst, 1);
+ dst.ptr += get_global_id(0) * dst_step_x + (get_global_id(1) % DST_HEIGHT) * dst_step_y + (get_global_id(1) / DST_HEIGHT) * dst_step_z + get_global_id(2) * dst_step_w;
+#else // defined(DST_HEIGHT)
Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst);
+#endif // defined(DST_HEIGHT)
+
#if defined(ADD_BIAS)
Vector biases = CONVERT_TO_VECTOR_STRUCT(biases);
#endif // defined(ADD_BIAS)
diff --git a/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp b/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp
index 875e26d..d403d67 100644
--- a/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp
+++ b/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp
@@ -27,9 +27,12 @@
#include "arm_compute/core/CL/ICLTensor.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/Window.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+
#include "support/ToolchainSupport.h"
using namespace arm_compute;
@@ -38,7 +41,8 @@
{
namespace
{
-Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max)
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output,
+ int min, int max, unsigned int output_3d_depth)
{
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32);
ARM_COMPUTE_RETURN_ERROR_ON(max > 255);
@@ -54,8 +58,10 @@
if(output->total_size() != 0)
{
+ const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_output_stage_shape(*input, output_3d_depth, true);
+ const TensorInfo tensor_info_output = output->clone()->set_tensor_shape(output_shape);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output);
}
return Status{};
@@ -66,7 +72,7 @@
constexpr unsigned int num_elems_processed_per_iteration = 16;
// Configure kernel window
- Window win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration));
+ Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration));
AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration);
@@ -75,8 +81,9 @@
if(output->total_size() != 0)
{
+ Window win_out = calculate_max_window(*output, Steps(num_elems_processed_per_iteration));
AccessWindowHorizontal output_result_access(output, 0, num_elems_processed_per_iteration);
- window_changed = window_changed || update_window_and_padding(win, output_result_access);
+ window_changed = window_changed || update_window_and_padding(win_out, output_result_access);
output_result_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
}
@@ -96,14 +103,15 @@
} // namespace arm_compute
CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel()
- : _input(nullptr), _bias(nullptr), _output(nullptr)
+ : _input(nullptr), _bias(nullptr), _output(nullptr), _reinterpret_as_3d(false)
{
}
-Status CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max)
+Status CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output,
+ int min, int max, unsigned int output_3d_depth)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
- ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, min, max));
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, min, max, output_3d_depth));
ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(),
(bias != nullptr) ? bias->clone().get() : nullptr,
output->clone().get())
@@ -112,24 +120,24 @@
return Status{};
}
-void CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, int result_fixedpoint_multiplier, int result_shift,
- int result_offset_after_shift, int min, int max)
+void CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output,
+ int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift,
+ int min, int max, unsigned int output_3d_depth)
{
// Perform validate step
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
// Output auto inizialitation if not yet initialized
- auto_init_if_empty(*output->info(), input->info()->clone()->set_data_type(DataType::QASYMM8));
+ const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_output_stage_shape(*input->info(), output_3d_depth, true);
+ auto_init_if_empty(*output->info(), input->info()->clone()->set_data_type(DataType::QASYMM8).set_tensor_shape(output_shape));
- ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(),
- (bias != nullptr) ? bias->info() : nullptr,
- output->info(),
- min,
- max));
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (bias != nullptr) ? bias->info() : nullptr, output->info(),
+ min, max, output_3d_depth));
- _input = input;
- _bias = bias;
- _output = output;
+ _input = input;
+ _bias = bias;
+ _output = output;
+ _reinterpret_as_3d = output_3d_depth > 1;
// Set the arguments to pass at compile time
CLBuildOptions build_opts;
@@ -139,6 +147,7 @@
build_opts.add_option_if((min != 0) && (min != max), "-DMIN_BOUND=" + support::cpp11::to_string(min));
build_opts.add_option_if((max != 255) && (min != max), "-DMAX_BOUND=" + support::cpp11::to_string(max));
build_opts.add_option_if(bias != nullptr, "-DADD_BIAS");
+ build_opts.add_option_if(_reinterpret_as_3d, "-DDST_HEIGHT=" + support::cpp11::to_string(input->info()->tensor_shape().y() / output_3d_depth));
// Create kernel
_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("gemmlowp_output_stage_quantize_down_fixedpoint", build_opts.options()));
@@ -154,9 +163,11 @@
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
+ // Create input window
Window collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ);
Window slice = collapsed.first_slice_window_3D();
+ // Setup bias slice
unsigned int idx1 = num_arguments_per_3D_tensor();
if(_bias != nullptr)
{
@@ -166,12 +177,32 @@
add_1D_tensor_argument(idx1, _bias, biases_slice);
}
- do
+ if(_reinterpret_as_3d)
{
- unsigned int idx = 0;
- add_3D_tensor_argument(idx, _input, slice);
- add_3D_tensor_argument(idx1, _output, slice);
- enqueue(queue, *this, slice);
+ // Create output window
+ Window window_out;
+ window_out.use_tensor_dimensions(_output->info()->tensor_shape());
+ Window collapsed_out = window_out.collapse_if_possible(window_out, 3);
+ Window slice_out = collapsed.first_slice_window_4D();
+
+ do
+ {
+ unsigned int idx = 0;
+ add_3D_tensor_argument(idx, _input, slice);
+ add_4D_tensor_argument(idx1, _output, slice_out);
+ enqueue(queue, *this, slice);
+ }
+ while(collapsed.slide_window_slice_3D(slice) && collapsed_out.slide_window_slice_4D(slice_out));
}
- while(collapsed.slide_window_slice_3D(slice));
+ else
+ {
+ do
+ {
+ unsigned int idx = 0;
+ add_3D_tensor_argument(idx, _input, slice);
+ add_3D_tensor_argument(idx1, _output, slice);
+ enqueue(queue, *this, slice);
+ }
+ while(collapsed.slide_window_slice_3D(slice));
+ }
}
diff --git a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp
index 0196bac..7cd50cc 100644
--- a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp
+++ b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp
@@ -28,6 +28,7 @@
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/NEON/NEAsymm.h"
+#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/core/Validate.h"
@@ -43,9 +44,8 @@
namespace
{
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output,
- int min, int max, unsigned int gemm_3d_depth)
+ int min, int max, unsigned int output_3d_depth)
{
- ARM_COMPUTE_UNUSED(gemm_3d_depth);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32);
ARM_COMPUTE_RETURN_ERROR_ON(max > 255);
ARM_COMPUTE_RETURN_ERROR_ON(min < 0 || min > max);
@@ -60,21 +60,10 @@
if(output->total_size() != 0)
{
- const TensorShape ref_shape = output->tensor_shape();
- const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_output_stage_shape(*input, gemm_3d_depth);
- // Check in case of mismatching dimensions when permuting, usually in case of 1x1xC input shapes
- if(output_shape.num_dimensions() != ref_shape.num_dimensions() && ref_shape.num_dimensions() < 4)
- {
- for(unsigned int i = output_shape.num_dimensions(); i < ref_shape.num_dimensions(); ++i)
- {
- ARM_COMPUTE_RETURN_ERROR_ON(ref_shape[i] != 1);
- }
- }
- else
- {
- ARM_COMPUTE_RETURN_ERROR_ON(output->tensor_shape() != output_shape);
- }
+ const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_output_stage_shape(*input, output_3d_depth);
+ const TensorInfo tensor_info_output = output->clone()->set_tensor_shape(output_shape);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output);
}
return Status{};
@@ -160,7 +149,7 @@
const int window_step_x = 16;
const auto window_start_x = static_cast<int>(window.x().start());
const auto window_end_x = static_cast<int>(window.x().end());
- const unsigned int gemm_3d_height = _input->info()->tensor_shape().y() / _gemm_3d_depth;
+ const unsigned int gemm_3d_height = _input->info()->tensor_shape().y() / _output_3d_depth;
Window win_collapsed = window.collapse_if_possible(window, Window::DimZ);
win_collapsed.set(Window::DimX, Window::Dimension(0, 1, 1));
@@ -177,7 +166,7 @@
{
// Calculate output coordinates
Coordinates out_coords = id;
- if(_gemm_3d_depth != 1)
+ if(_output_3d_depth != 1)
{
out_coords.set(Window::DimY, id.y() % gemm_3d_height);
out_coords.set(Window::DimZ, id.y() / gemm_3d_height);
@@ -240,10 +229,10 @@
{
// Calculate output coordinates
Coordinates out_coords = id;
- if(_gemm_3d_depth != 1)
+ if(_output_3d_depth != 1)
{
- out_coords.set(Window::DimY, id.y() % _gemm_3d_depth);
- out_coords.set(Window::DimZ, id.y() / _gemm_3d_depth);
+ out_coords.set(Window::DimY, id.y() % _output_3d_depth);
+ out_coords.set(Window::DimZ, id.y() / _output_3d_depth);
out_coords.set(3, id.z());
}
uint8_t *out_ptr = _output->ptr_to_element(out_coords);
@@ -279,22 +268,22 @@
}
NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel()
- : _func(nullptr), _input(nullptr), _bias(nullptr), _output(nullptr), _result_fixedpoint_multiplier(0), _result_shift(0), _result_offset_after_shift(0), _min(0), _max(0), _gemm_3d_depth(1)
+ : _func(nullptr), _input(nullptr), _bias(nullptr), _output(nullptr), _result_fixedpoint_multiplier(0), _result_shift(0), _result_offset_after_shift(0), _min(0), _max(0), _output_3d_depth(1)
{
}
void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_fixedpoint_multiplier, int result_shift,
- int result_offset_after_shift, int min, int max, unsigned int gemm_3d_depth)
+ int result_offset_after_shift, int min, int max, unsigned int output_3d_depth)
{
// Perform validate step
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
// Output auto inizialitation if not yet initialized
- const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_output_stage_shape(*input->info(), gemm_3d_depth);
+ const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_output_stage_shape(*input->info(), output_3d_depth);
auto_init_if_empty(*output->info(), input->info()->clone()->set_data_type(DataType::QASYMM8).set_tensor_shape(output_shape));
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (bias != nullptr) ? bias->info() : nullptr, output->info(),
- min, max, gemm_3d_depth));
+ min, max, output_3d_depth));
_input = input;
_bias = bias;
@@ -304,7 +293,7 @@
_result_offset_after_shift = result_offset_after_shift;
_min = min;
_max = max;
- _gemm_3d_depth = gemm_3d_depth;
+ _output_3d_depth = output_3d_depth;
// Configure kernel window
auto win_config = validate_and_configure_window(input->info(), (bias != nullptr) ? bias->info() : nullptr, output->info());
@@ -316,10 +305,10 @@
_func = is_bounded_relu ? &NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run<true> : &NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::run<false>;
}
-Status NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max, unsigned int gemm_3d_depth)
+Status NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max, unsigned int output_3d_depth)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
- ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, min, max, gemm_3d_depth));
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, min, max, output_3d_depth));
ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(),
(bias != nullptr) ? bias->clone().get() : nullptr,
output->clone().get())
diff --git a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
index bd5e969..f41a12a 100644
--- a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
@@ -92,8 +92,8 @@
CLGEMMConvolutionLayer::CLGEMMConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
: _memory_group(memory_manager), _reshape_weights(), _im2col_kernel(), _mm_gemm(memory_manager), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(), _col2im_kernel(), _activationlayer_function(),
- _add_bias_kernel(), _reshape_layer(), _original_weights(nullptr), _im2col_output(), _weights_reshaped(), _gemm_output(), _tmp_output(), _data_layout(DataLayout::NCHW), _append_bias(false),
- _skip_im2col(false), _is_quantized(false), _is_activationlayer_enabled(false), _is_prepared(false)
+ _add_bias_kernel(), _original_weights(nullptr), _im2col_output(), _weights_reshaped(), _gemm_output(), _tmp_output(), _data_layout(DataLayout::NCHW), _append_bias(false), _skip_im2col(false),
+ _skip_col2im(false), _is_quantized(false), _is_activationlayer_enabled(false), _is_prepared(false)
{
}
@@ -102,6 +102,9 @@
ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights);
ARM_COMPUTE_ERROR_THROW_ON(validate_mm(input->info(), weights->info(), output->info(), gemm_3d_depth, _skip_im2col));
+ const GEMMInfo &gemm_info = GEMMInfo(false, false, true /* Reshape weights only for the first run */,
+ gemm_3d_depth, _skip_im2col /* Reinterpret the input as 3D if im2col is skipped */);
+
if(_is_quantized)
{
// Since we need negative offsets for computing convolution, we need to change QuantizationInfo()
@@ -112,7 +115,7 @@
input->info()->set_quantization_info(QuantizationInfo(input_quantization_info.scale, -input_quantization_info.offset));
weights->info()->set_quantization_info(QuantizationInfo(weights_quantization_info.scale, -weights_quantization_info.offset));
- _mm_gemmlowp.configure(input, weights, output, GEMMInfo(false, false, true /* Reshape weights only for the first run*/));
+ _mm_gemmlowp.configure(input, weights, output, gemm_info);
// Revert back QuantizatioInfo as input and weights could be used in other convolution layers
input->info()->set_quantization_info(input_quantization_info);
@@ -121,8 +124,7 @@
else
{
// Configure matrix multiply function
- _mm_gemm.configure(input, weights, nullptr, output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run*/, gemm_3d_depth,
- _skip_im2col /* Reinterpret the input as 3D if im2col is skipped */));
+ _mm_gemm.configure(input, weights, nullptr, output, 1.0f, 0.0f, gemm_info);
}
}
@@ -130,10 +132,11 @@
{
const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type());
+ const GEMMInfo &gemm_info = GEMMInfo(false, false, true /* Reshape weights only for the first run */,
+ gemm_3d_depth, skip_im2col /* Reinterpret the input as 3D if im2col is skipped */);
+
if(is_quantized)
{
- const GEMMInfo &gemm_info = GEMMInfo(false, false, true /* Reshape weights only for the first run */);
-
// Since we need negative offsets for computing convolution, we need to change QuantizationInfo()
// Extract and negate input and weights offset
const QuantizationInfo input_quantization_info = input->quantization_info();
@@ -149,8 +152,6 @@
}
else
{
- const GEMMInfo &gemm_info = GEMMInfo(false, false, true /* Reshape weights only for the first run */, gemm_3d_depth, skip_im2col /* Reinterpret the input as 3D if im2col is skipped */);
-
// Perform validation step on Matrix multiply function
return CLGEMM::validate(input, weights, nullptr, output, 1.0f, 0.0f, gemm_info);
}
@@ -175,6 +176,7 @@
const DataLayout data_layout = input->info()->data_layout();
const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+ const int idx_channel = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);
const int idx_kernels = get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES);
const unsigned int kernel_width = weights->info()->dimension(idx_width);
@@ -184,14 +186,14 @@
_original_weights = weights;
_is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type());
_data_layout = data_layout;
- _skip_im2col = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv_info.stride().first == 1 && conv_info.stride().second == 1) && !_is_quantized;
+ _skip_im2col = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv_info.stride().first == 1 && conv_info.stride().second == 1);
+ _skip_col2im = data_layout == DataLayout::NHWC;
_append_bias = (biases != nullptr) && (!_is_quantized);
// Set the GPU target for im2col and col2im
_im2col_kernel.set_target(CLScheduler::get().target());
_col2im_kernel.set_target(CLScheduler::get().target());
- bool is_nhwc = _data_layout == DataLayout::NHWC;
const ICLTensor *gemm_input_to_use = input;
ICLTensor *gemm_output_to_use = output;
ICLTensor *gemm_output_staged_to_use = output;
@@ -241,18 +243,27 @@
}
// Create GEMM output tensor
- if(!is_nhwc || _is_quantized)
+ if(!_skip_col2im || _is_quantized)
{
- // Calculate GEMM output shape
- TensorShape shape_gemm = _im2col_output.info()->tensor_shape();
- shape_gemm.set(0, mat_weights_cols);
- shape_gemm.set(1, conv_w * conv_h);
-
+ TensorShape shape_gemm;
+ if(_skip_col2im)
+ {
+ shape_gemm = input->info()->tensor_shape();
+ shape_gemm.set(idx_width, conv_w);
+ shape_gemm.set(idx_height, conv_h);
+ shape_gemm.set(idx_channel, mat_weights_cols);
+ }
+ else
+ {
+ shape_gemm = _im2col_output.info()->tensor_shape();
+ shape_gemm.set(0, mat_weights_cols);
+ shape_gemm.set(1, conv_w * conv_h);
+ }
// GEMM output should be S32 for acquiring raw integer accumulator without quantized postprocessing for quantized asymmetric input.
const DataType gemm_data_type = _is_quantized ? DataType::S32 : data_type;
// FIXME: input->clone() doesn't work with subtensors for grouped convolutions.
TensorInfo info_gemm(shape_gemm, 1, gemm_data_type);
- info_gemm.set_quantization_info(output->info()->quantization_info());
+ info_gemm.set_quantization_info(output->info()->quantization_info()).set_data_layout(input->info()->data_layout());
_gemm_output.allocator()->init(info_gemm);
_memory_group.manage(&_gemm_output);
@@ -277,30 +288,29 @@
int output_multiplier, output_shift;
quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
- _memory_group.manage(&_tmp_output);
- gemm_output_staged_to_use = &_tmp_output;
+ if(!_skip_col2im)
+ {
+ _memory_group.manage(&_tmp_output);
+ gemm_output_staged_to_use = &_tmp_output;
+ }
_gemmlowp_output_stage.configure(gemm_output_to_use, biases, gemm_output_staged_to_use, output_multiplier, output_shift, output_quant_info.offset);
}
- if(!is_nhwc || _is_quantized)
+ if(!_skip_col2im)
{
- if(input->info()->data_layout() == DataLayout::NCHW)
- {
- // Configure and tune Col2Im
- _col2im_kernel.configure(_is_quantized ? gemm_output_staged_to_use : gemm_output_to_use, output, Size2D(conv_w, conv_h), num_groups);
- CLScheduler::get().tune_kernel_static(_col2im_kernel);
- }
- else
- {
- // Configure reshape layer
- _reshape_layer.configure(_is_quantized ? gemm_output_staged_to_use : gemm_output_to_use, output);
- }
+ // Configure and tune Col2Im
+ _col2im_kernel.configure(_is_quantized ? gemm_output_staged_to_use : gemm_output_to_use, output, Size2D(conv_w, conv_h), num_groups);
+ CLScheduler::get().tune_kernel_static(_col2im_kernel);
}
- if(!is_nhwc || _is_quantized)
+ if(!_skip_col2im)
{
_tmp_output.allocator()->allocate();
+ }
+
+ if(!_skip_col2im || _is_quantized)
+ {
_gemm_output.allocator()->allocate();
}
@@ -346,10 +356,10 @@
const ITensorInfo *gemm_output_staged_to_use = output;
const ITensorInfo *weights_to_use = weights;
- const bool is_nhwc = data_layout == DataLayout::NHWC;
const bool is_quantized = is_data_type_quantized_asymmetric(data_type);
- const bool skip_im2col = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv_info.stride().first == 1 && conv_info.stride().second == 1) && !is_quantized;
const bool append_bias = (biases != nullptr) && (!is_quantized);
+ const bool skip_im2col = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv_info.stride().first == 1 && conv_info.stride().second == 1);
+ const bool skip_col2im = data_layout == DataLayout::NHWC;
ARM_COMPUTE_RETURN_ERROR_ON((weights->dimension(idx_channel) * num_groups) != input->dimension(idx_channel));
ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
@@ -411,19 +421,30 @@
}
// Create GEMM output tensor
- if(!is_nhwc || is_quantized)
+ if(!skip_col2im || is_quantized)
{
- TensorShape shape_gemm = gemm_input_to_use->tensor_shape();
- shape_gemm.set(0, mat_weights_cols);
- shape_gemm.set(1, conv_w * conv_h);
const DataType gemm_data_type = is_quantized ? DataType::S32 : data_type;
+ TensorShape shape_gemm;
+ if(skip_col2im)
+ {
+ shape_gemm = input->tensor_shape();
+ shape_gemm.set(idx_width, conv_w);
+ shape_gemm.set(idx_height, conv_h);
+ shape_gemm.set(idx_channel, mat_weights_cols);
+ }
+ else
+ {
+ shape_gemm = gemm_input_to_use->tensor_shape();
+ shape_gemm.set(0, mat_weights_cols);
+ shape_gemm.set(1, conv_w * conv_h);
+ }
// GEMM output should be S32 for acquiring raw integer accumulator without quantized postprocessing for quantized asymmetric input.
info_gemm = TensorInfo(shape_gemm, 1, gemm_data_type);
- info_gemm.set_quantization_info(output->quantization_info());
+ info_gemm.set_quantization_info(output->quantization_info()).set_data_layout(input->data_layout());
gemm_output_to_use = &info_gemm;
}
- ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemm_input_to_use, weights_to_use, gemm_output_to_use, (data_layout == DataLayout::NHWC) ? conv_h : 1, skip_im2col));
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemm_input_to_use, weights_to_use, gemm_output_to_use, skip_col2im ? conv_h : 1, skip_im2col));
if(is_quantized)
{
@@ -431,23 +452,22 @@
int output_multiplier, output_shift;
quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
- tmp_info = TensorInfo(gemm_output_to_use->tensor_shape(), 1, DataType::QASYMM8);
- tmp_info.set_quantization_info(output->quantization_info());
- gemm_output_staged_to_use = &tmp_info;
+ if(!skip_col2im)
+ {
+ tmp_info = TensorInfo(gemm_output_to_use->tensor_shape(), 1, DataType::QASYMM8);
+ tmp_info.set_quantization_info(output->quantization_info());
+ gemm_output_staged_to_use = &tmp_info;
+ }
// Validate output stage for quantized case
- CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(gemm_output_to_use, biases, gemm_output_staged_to_use, output->quantization_info().offset);
+ CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(gemm_output_to_use, biases, gemm_output_staged_to_use);
}
// Validate Col2Im
- if(!is_nhwc || is_quantized)
+ if(!skip_col2im)
{
- if(input->data_layout() == DataLayout::NCHW)
- {
- ARM_COMPUTE_RETURN_ON_ERROR(CLCol2ImKernel::validate(is_quantized ? gemm_output_staged_to_use : gemm_output_to_use,
- output,
- Size2D(conv_w, conv_h), num_groups));
- }
+ ARM_COMPUTE_RETURN_ON_ERROR(CLCol2ImKernel::validate(is_quantized ? gemm_output_staged_to_use : gemm_output_to_use, output,
+ Size2D(conv_w, conv_h), num_groups));
}
//Validate Activation Layer
@@ -492,16 +512,9 @@
}
// Reshape output matrix
- if(_data_layout == DataLayout::NCHW || _is_quantized)
+ if(!_skip_col2im)
{
- if(_data_layout == DataLayout::NCHW)
- {
- CLScheduler::get().enqueue(_col2im_kernel, false);
- }
- else
- {
- _reshape_layer.run();
- }
+ CLScheduler::get().enqueue(_col2im_kernel, false);
}
//Run Activation Layer if enabled
diff --git a/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp b/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp
index 1d6f343..62e7ee7 100644
--- a/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp
+++ b/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp
@@ -108,7 +108,7 @@
// If we pass the matrix A and matrix B reshaped to CLGEMMMatrixMultiplyKernel, we need to pass m, n, k, mult_transpose1xW_width and mult_interleave4x4_height to CLGEMMReshapeInfo
// in order to know how the matrices have been reshaped
bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();
- const int m = a->info()->dimension(1);
+ const int m = reinterpret_input_as_3d ? (a->info()->dimension(1) * a->info()->dimension(2)) : a->info()->dimension(1);
const int n = b->info()->dimension(0);
const int k = a->info()->dimension(0);
const int depth_output_gemm3d = gemm_info.depth_output_gemm3d();
@@ -206,12 +206,12 @@
int32_t a_offset = a->quantization_info().offset;
int32_t b_offset = b->quantization_info().offset;
- const int m = a->dimension(1);
+ bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();
+ const int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1);
const int n = b->dimension(0);
const int k = a->dimension(0);
constexpr int mult_transpose1xW_width = 1;
constexpr int mult_interleave4x4_height = 1;
- bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();
const int depth_output_gemm3d = gemm_info.depth_output_gemm3d();
bool reshape_matrices = is_interleaved_transposed(m, n, k, gemm_info.reshape_b_only_on_first_run(), CLScheduler::get().target());
diff --git a/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp b/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp
index 16d8678..b18d23f 100644
--- a/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp
+++ b/src/runtime/CL/functions/CLGEMMLowpOutputStage.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -28,8 +28,8 @@
#include "arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.h"
#include "support/ToolchainSupport.h"
-using namespace arm_compute;
-
+namespace arm_compute
+{
void CLGEMMLowpQuantizeDownInt32ToUint8Scale::configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, int result_offset, int result_mult_int, int result_shift, int min, int max)
{
auto k = arm_compute::support::cpp14::make_unique<CLGEMMLowpQuantizeDownInt32ToUint8ScaleKernel>();
@@ -42,15 +42,18 @@
return CLGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::validate(input, bias, output, min, max);
}
-void CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, int result_fixedpoint_multiplier, int result_shift,
- int result_offset_after_shift, int min, int max)
+void CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output,
+ int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift,
+ int min, int max, unsigned int output_3d_depth)
{
auto k = arm_compute::support::cpp14::make_unique<CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel>();
- k->configure(input, bias, output, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max);
+ k->configure(input, bias, output, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max, output_3d_depth);
_kernel = std::move(k);
}
-Status CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max)
+Status CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output,
+ int min, int max, unsigned int output_3d_depth)
{
- return CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(input, bias, output, min, max);
-}
\ No newline at end of file
+ return CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(input, bias, output, min, max, output_3d_depth);
+}
+} // namespace arm_compute
\ No newline at end of file
diff --git a/src/runtime/NEON/functions/NEGEMMLowpOutputStage.cpp b/src/runtime/NEON/functions/NEGEMMLowpOutputStage.cpp
index cb70049..d270a77 100644
--- a/src/runtime/NEON/functions/NEGEMMLowpOutputStage.cpp
+++ b/src/runtime/NEON/functions/NEGEMMLowpOutputStage.cpp
@@ -43,14 +43,14 @@
}
void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::configure(const ITensor *input, const ITensor *bias, ITensor *output, int result_fixedpoint_multiplier, int result_shift,
- int result_offset_after_shift, int min, int max, unsigned int gemm_3d_depth)
+ int result_offset_after_shift, int min, int max, unsigned int output_3d_depth)
{
auto k = arm_compute::support::cpp14::make_unique<NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel>();
- k->configure(input, bias, output, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max, gemm_3d_depth);
+ k->configure(input, bias, output, result_fixedpoint_multiplier, result_shift, result_offset_after_shift, min, max, output_3d_depth);
_kernel = std::move(k);
}
-Status NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max, unsigned int gemm_3d_depth)
+Status NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max, unsigned int output_3d_depth)
{
- return NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(input, bias, output, min, max, gemm_3d_depth);
+ return NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(input, bias, output, min, max, output_3d_depth);
}
\ No newline at end of file