arm_compute v18.05
diff --git a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
index a85078c..2888b43 100644
--- a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
@@ -23,9 +23,6 @@
*/
#include "arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h"
-#include "arm_compute/core/NEON/kernels/arm32/NEGEMMAArch32Kernel.h"
-#include "arm_compute/core/NEON/kernels/arm64/NEGEMMAArch64Kernel.h"
-#include "arm_compute/core/NEON/kernels/arm64/NEGEMMAArch64NativeKernel.h"
#include "arm_compute/core/PixelValue.h"
#include "arm_compute/core/Size2D.h"
#include "arm_compute/core/Utils.h"
@@ -34,13 +31,6 @@
#include "arm_compute/runtime/NEON/NEScheduler.h"
#include "support/ToolchainSupport.h"
-namespace arm_compute
-{
-#include "arm_compute/core/NEON/kernels/assembly/gemm_interleaved.hpp"
-#include "arm_compute/core/NEON/kernels/assembly/kernels/a32_sgemm_8x6.hpp"
-#include "arm_compute/core/NEON/kernels/assembly/kernels/a64_sgemm_12x8.hpp"
-} // namespace arm_compute
-
#include <cmath>
#include <tuple>
@@ -175,19 +165,28 @@
}
}
-Status validate_and_initialize_values(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, DataType &dt,
- bool &append_bias,
+Status validate_and_initialize_values(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const PadStrideInfo &conv_info, const WeightsInfo &weights_info,
+ const ActivationLayerInfo &act_info, DataType &dt,
+ bool &append_bias, bool &skip_im2col,
bool &are_weights_reshaped, unsigned int &kernel_width, unsigned int &kernel_height,
- bool &is_fully_connected_convolution, bool &is_interleaved, bool &is_quantized,
+ bool &is_fully_connected_convolution, bool &is_interleaved, bool &is_quantized, bool &is_activationlayer_enabled,
unsigned int &mat_weights_cols, unsigned int &mat_weights_rows,
- unsigned int &conv_w, unsigned int &conv_h)
+ unsigned int &conv_w, unsigned int &conv_h, const Size2D &dilation)
{
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, weights);
- ARM_COMPUTE_RETURN_ERROR_ON(!weights_info.are_reshaped() && weights->dimension(2) != input->dimension(2));
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, weights);
+
+ DataLayout data_layout = input->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);
+
+ ARM_COMPUTE_RETURN_ERROR_ON(!weights_info.are_reshaped() && weights->dimension(idx_channel) != input->dimension(idx_channel));
ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
ARM_COMPUTE_RETURN_ERROR_ON(weights_info.are_reshaped() && is_data_type_quantized_asymmetric(input->data_type()));
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(data_layout == DataLayout::NHWC && input->data_type() != DataType::F32, "NHWC is only supported for FP32 data type.");
dt = input->data_type();
is_quantized = is_data_type_quantized_asymmetric(dt);
@@ -207,28 +206,32 @@
ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
}
+ // If we have 1x1 convolution and data layout is NHWC we can disable im2col
append_bias = (biases != nullptr) && (!is_quantized);
are_weights_reshaped = weights_info.are_reshaped();
- kernel_width = (are_weights_reshaped) ? weights_info.kernel_size().first : weights->dimension(0);
- kernel_height = (are_weights_reshaped) ? weights_info.kernel_size().second : weights->dimension(1);
+ kernel_width = (are_weights_reshaped) ? weights_info.kernel_size().first : weights->dimension(idx_width);
+ kernel_height = (are_weights_reshaped) ? weights_info.kernel_size().second : weights->dimension(idx_height);
mat_weights_cols = weights->dimension(3);
- mat_weights_rows = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + (append_bias ? 1 : 0);
+ mat_weights_rows = weights->dimension(idx_width) * weights->dimension(idx_height) * weights->dimension(idx_channel) + ((append_bias && !skip_im2col) ? 1 : 0);
+ skip_im2col = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1);
- std::tie(conv_w, conv_h) = scaled_dimensions(input->dimension(0), input->dimension(1), kernel_width, kernel_height,
- conv_info);
+ std::tie(conv_w, conv_h) = scaled_dimensions(input->dimension(idx_width), input->dimension(idx_height), kernel_width, kernel_height,
+ conv_info, dilation);
// Check if its a "fully connected" convolution
is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1));
is_interleaved = (!is_fully_connected_convolution && !is_quantized);
+ is_activationlayer_enabled = act_info.enabled();
return Status{};
}
} // namespace
NEGEMMConvolutionLayer::NEGEMMConvolutionLayer(const std::shared_ptr<IMemoryManager> &memory_manager)
- : _memory_group(memory_manager), _input_im2col_kernel(), _input_interleave_kernel(), _reshape_weights(), _mm_kernel(), _mm_optimised_kernel(nullptr), _mm_gemmlowp(memory_manager),
- _gemmlowp_output_stage(), _output_col2im_kernel(), _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _gemm_output(), _tmp_output(), _workspace(), _append_bias(false),
- _is_fully_connected_convolution(false), _are_weights_reshaped(false), _is_quantized(false), _is_interleaved(false)
+ : _asm_glue(), _memory_group(memory_manager), _input_im2col_kernel(), _input_interleave_kernel(), _reshape_weights(), _mm_kernel(), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(),
+ _output_col2im_kernel(), _activationlayer_function(), _add_bias_kernel(), _original_weights(nullptr), _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _gemm_output(),
+ _tmp_output(), _workspace(), _B_pretransposed(), _data_layout(DataLayout::NCHW), _append_bias(false), _is_fully_connected_convolution(false), _are_weights_reshaped(false), _is_quantized(false),
+ _is_interleaved(false), _is_activationlayer_enabled(false), _skip_im2col(false)
{
}
@@ -256,26 +259,8 @@
}
}
-void NEGEMMConvolutionLayer::configure_asm_mm(const struct CPUInfo &ci, int M, int N, int K)
-{
- ARM_COMPUTE_UNUSED(ci);
- ARM_COMPUTE_UNUSED(M);
- ARM_COMPUTE_UNUSED(N);
- ARM_COMPUTE_UNUSED(K);
-#if defined(__arm__) || defined(__aarch64__)
-#if defined(__arm__)
- GemmInterleaved<sgemm_8x6, float, float> gemm(&ci, M, N, K, false, false);
-#elif defined(__aarch64__)
- GemmInterleaved<sgemm_12x8, float, float> gemm(&ci, M, N, K, false, false);
-#endif /* defined(__arm__) || defined(__aarch64__) */
-
- constexpr size_t alignment = 4096;
- _workspace.allocator()->init(TensorInfo(TensorShape{ (gemm.get_working_size() + alignment - 1) * NEScheduler::get().num_threads() }, 1, DataType::U8));
- _memory_group.manage(&_workspace);
-#endif /* defined(__arm__) || defined(__aarch64__) */
-}
-
-void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
+void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info,
+ const Size2D &dilation, const ActivationLayerInfo &act_info)
{
// Perform validate step
ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
@@ -288,45 +273,35 @@
unsigned int conv_w = 0;
unsigned int conv_h = 0;
- Status status = validate_and_initialize_values(input->info(), weights->info(), (biases == nullptr) ? nullptr : biases->info(), conv_info, weights_info, dt, _append_bias, _are_weights_reshaped,
+ _data_layout = input->info()->data_layout();
+ const bool is_nhwc = _data_layout == DataLayout::NHWC;
+ 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);
+
+ Status status = validate_and_initialize_values(input->info(), weights->info(), (biases == nullptr) ? nullptr : biases->info(), conv_info, weights_info, act_info, dt, _append_bias, _skip_im2col,
+ _are_weights_reshaped,
kernel_width, kernel_height,
- _is_fully_connected_convolution, _is_interleaved, _is_quantized,
- mat_weights_cols, mat_weights_rows, conv_w, conv_h);
+ _is_fully_connected_convolution, _is_interleaved, _is_quantized, _is_activationlayer_enabled,
+ mat_weights_cols, mat_weights_rows, conv_w, conv_h, dilation);
ARM_COMPUTE_ERROR_THROW_ON(status);
+ _original_weights = weights;
const unsigned int fixed_point_position = input->info()->fixed_point_position();
const ITensor *biases_to_use = (_append_bias) ? biases : nullptr;
-#if defined(__arm__)
- if(NEScheduler::get().cpu_info().CPU == CPUTarget::ARMV7 && dt == DataType::F32)
- {
- _mm_optimised_kernel = support::cpp14::make_unique<NEGEMMAArch32Kernel>();
- }
-#elif defined(__aarch64__)
- if(NEScheduler::get().cpu_info().CPU >= CPUTarget::ARMV8 && dt == DataType::F32)
- {
- _mm_optimised_kernel = support::cpp14::make_unique<NEGEMMAArch64Kernel>();
- }
-#endif /* defined(__arm__) || defined(__aarch64__) */
+ bool run_optimised = dt == DataType::F32;
// Reshape weights if needed
- if(_mm_optimised_kernel != nullptr)
+ if(run_optimised)
{
- if(_are_weights_reshaped)
- {
- mat_weights_cols = weights_info.num_kernels();
- mat_weights_rows = weights->info()->dimension(1);
- }
- else
- {
- TensorShape reshaped_weights_shape{ mat_weights_cols, mat_weights_rows };
+ TensorShape reshaped_weights_shape{ mat_weights_cols, mat_weights_rows };
- // Create tensor to store the reshaped weights
- _weights_reshaped.allocator()->init(TensorInfo(reshaped_weights_shape, 1, dt, fixed_point_position));
- _reshape_weights.configure(weights, biases, &_weights_reshaped, false /* 1xW transpose */);
- weights = &_weights_reshaped;
- }
+ // Create tensor to store the reshaped weights
+ _weights_reshaped.allocator()->init(TensorInfo(reshaped_weights_shape, 1, dt, fixed_point_position));
+ _reshape_weights.configure(weights, biases, &_weights_reshaped, false /* 1xW transpose */);
+ weights = &_weights_reshaped;
}
else
{
@@ -335,12 +310,12 @@
if(_is_fully_connected_convolution || _is_quantized)
{
mat_weights_cols = weights_info.num_kernels();
- mat_weights_rows = weights->info()->dimension(1);
+ mat_weights_rows = weights->info()->dimension(idx_height);
}
else
{
mat_weights_cols = weights_info.num_kernels();
- mat_weights_rows = weights_info.kernel_size().first * weights_info.kernel_size().second * input->info()->dimension(2) + (_append_bias ? 1 : 0);
+ mat_weights_rows = weights_info.kernel_size().first * weights_info.kernel_size().second * input->info()->dimension(idx_channel) + (_append_bias ? 1 : 0);
}
}
else
@@ -366,66 +341,56 @@
}
}
- // Create tensor to store im2col reshaped inputs
- const unsigned int mat_input_cols = mat_weights_rows;
- const unsigned int mat_input_rows = conv_w * conv_h;
-
- TensorShape shape_im2col(input->info()->tensor_shape());
- shape_im2col.set(0, mat_input_cols);
- shape_im2col.set(1, mat_input_rows);
- shape_im2col.set(2, 1);
- _input_im2col_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col));
- _memory_group.manage(&_input_im2col_reshaped);
-
- // Create tensor (interleave) to prepare input tensor for GEMM
- if(!_is_fully_connected_convolution && _mm_optimised_kernel == nullptr)
+ // In case we skip im2col we have to add bias
+ if(!_skip_im2col)
{
- TensorShape shape_interleaved(shape_im2col);
- shape_interleaved.set(0, shape_interleaved.x() * 4);
- shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f));
- _input_interleaved_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_interleaved));
- _memory_group.manage(&_input_interleaved_reshaped);
+ const unsigned int mat_input_cols = mat_weights_rows;
+ const unsigned int mat_input_rows = conv_w * conv_h;
+
+ // Create tensor to store im2col reshaped inputs
+ TensorShape shape_im2col(input->info()->tensor_shape());
+ shape_im2col.set(0, mat_input_cols);
+ shape_im2col.set(1, mat_input_rows);
+ shape_im2col.set(2, 1);
+ _input_im2col_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col));
+ _memory_group.manage(&_input_im2col_reshaped);
+
+ // Create tensor (interleave) to prepare input tensor for GEMM
+ if(!_is_fully_connected_convolution && !run_optimised && _is_interleaved)
+ {
+ TensorShape shape_interleaved(shape_im2col);
+ shape_interleaved.set(idx_width, shape_interleaved.x() * 4);
+ shape_interleaved.set(idx_height, std::ceil(shape_interleaved[idx_height] / 4.f));
+ _input_interleaved_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_interleaved));
+ _memory_group.manage(&_input_interleaved_reshaped);
+ }
+
+ // Create GEMM output tensor
+ TensorShape shape_gemm(_input_im2col_reshaped.info()->tensor_shape());
+ shape_gemm.set(0, mat_weights_cols);
+ shape_gemm.set(1, mat_input_rows);
+ const DataType gemm_data_type = _is_quantized ? DataType::S32 : dt;
+ // GEMM output should be S32 for acquiring raw integer accumulator without quantized postprocessing for quantized asymmetric input.
+ TensorInfo info_gemm(shape_gemm, 1, gemm_data_type, input->info()->fixed_point_position());
+ info_gemm.set_quantization_info(output->info()->quantization_info());
+ _gemm_output.allocator()->init(info_gemm);
+
+ // Configure im2col
+ _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _append_bias, false, false, dilation);
+ }
+ else if(_append_bias)
+ {
+ // Configure add bias kernel
+ _add_bias_kernel.configure(output, biases, output, ConvertPolicy::SATURATE);
}
- // Create GEMM output tensor
- TensorShape shape_gemm(_input_im2col_reshaped.info()->tensor_shape());
- shape_gemm.set(0, mat_weights_cols);
- shape_gemm.set(1, mat_input_rows);
- const DataType gemm_data_type = _is_quantized ? DataType::S32 : dt;
- // GEMM output should be S32 for acquiring raw integer accumulator without quantized postprocessing for quantized asymmetric input.
- TensorInfo info_gemm(shape_gemm, 1, gemm_data_type, input->info()->fixed_point_position());
- info_gemm.set_quantization_info(output->info()->quantization_info());
- _gemm_output.allocator()->init(info_gemm);
- _memory_group.manage(&_gemm_output);
-
- // Configure kernels
- // Configure im2col
- _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _append_bias);
-
// Configure matrix multiply
- if(_mm_optimised_kernel != nullptr)
+ if(run_optimised)
{
- struct CPUInfo ci = NEScheduler::get().cpu_info();
-
- const int M = _gemm_output.info()->tensor_shape().y();
- const int N = _gemm_output.info()->tensor_shape().x();
- const int K = _input_im2col_reshaped.info()->tensor_shape().x();
-
-#if defined(__aarch64__)
- if((N <= 128) && (K <= 128))
+ if(!setup_assembly_kernel(_skip_im2col ? input : &_input_im2col_reshaped, weights, is_nhwc ? output : &_gemm_output, 1.f, 0.f, true, _workspace, _B_pretransposed, _memory_group, _asm_glue))
{
- _mm_optimised_kernel = support::cpp14::make_unique<NEGEMMAArch64NativeKernel>();
+ ARM_COMPUTE_ERROR("setup_assembly_kernel failed.");
}
- else
-#endif /* defined(__aarch64__) */
- {
- configure_asm_mm(ci, M, N, K);
- }
-
- // Configure matrix multiplication kernel
- _mm_optimised_kernel->configure(&_input_im2col_reshaped, weights, &_gemm_output, &_workspace);
-
- _workspace.allocator()->allocate();
}
else
{
@@ -435,8 +400,8 @@
_input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped);
// Configure GEMM
- configure_mm(&_input_interleaved_reshaped, weights, &_gemm_output, _is_interleaved, GEMMReshapeInfo(_input_im2col_reshaped.info()->dimension(1), 0 /* no transpose */,
- _input_im2col_reshaped.info()->dimension(0)));
+ configure_mm(&_input_interleaved_reshaped, weights, &_gemm_output, _is_interleaved, GEMMReshapeInfo(_input_im2col_reshaped.info()->dimension(idx_height), 0 /* no transpose */,
+ _input_im2col_reshaped.info()->dimension(idx_width)));
_input_interleaved_reshaped.allocator()->allocate();
}
else
@@ -445,48 +410,63 @@
}
}
- _input_im2col_reshaped.allocator()->allocate();
-
- // Configure output stage for quantized case
- if(_is_quantized)
+ if(!_skip_im2col)
{
- const QuantizationInfo output_quant_info = (output->info()->total_size() == 0) ? input->info()->quantization_info() : output->info()->quantization_info();
+ _input_im2col_reshaped.allocator()->allocate();
- float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / output_quant_info.scale;
- int output_multiplier, output_shift;
- quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
- _memory_group.manage(&_tmp_output);
- _gemmlowp_output_stage.configure(&_gemm_output, biases, &_tmp_output, output_multiplier, output_shift, output_quant_info.offset);
+ // Configure output stage for quantized case
+ if(_is_quantized)
+ {
+ const QuantizationInfo output_quant_info = (output->info()->total_size() == 0) ? input->info()->quantization_info() : output->info()->quantization_info();
+
+ float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / output_quant_info.scale;
+ int output_multiplier, output_shift;
+ quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
+ _memory_group.manage(&_tmp_output);
+ _gemmlowp_output_stage.configure(&_gemm_output, biases, &_tmp_output, output_multiplier, output_shift, output_quant_info.offset);
+ }
+
+ // Configure Col2Im
+ if(!is_nhwc)
+ {
+ _output_col2im_kernel.configure(_is_quantized ? &_tmp_output : &_gemm_output, output, Size2D(conv_w, conv_h));
+ }
+
+ if(_is_quantized)
+ {
+ _tmp_output.allocator()->allocate();
+ }
+ _gemm_output.allocator()->allocate();
}
- // Configure Col2Im
- _output_col2im_kernel.configure(_is_quantized ? &_tmp_output : &_gemm_output, output, Size2D(conv_w, conv_h));
- if(_is_quantized)
- {
- _tmp_output.allocator()->allocate();
- }
- _gemm_output.allocator()->allocate();
-
- ARM_COMPUTE_ERROR_ON_MSG((output->info()->dimension(0) != conv_w) || (output->info()->dimension(1) != conv_h), "Output shape does not match the expected one");
+ ARM_COMPUTE_ERROR_ON_MSG((output->info()->dimension(idx_width) != conv_w) || (output->info()->dimension(idx_height) != conv_h), "Output shape does not match the expected one");
// Allocate intermediate tensor
if(!_are_weights_reshaped)
{
_weights_reshaped.allocator()->allocate();
}
+
+ //Configure Activation Layer
+ if(_is_activationlayer_enabled)
+ {
+ _activationlayer_function.configure(output, nullptr, act_info);
+ }
}
Status NEGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
- const WeightsInfo &weights_info)
+ const WeightsInfo &weights_info, const Size2D &dilation, const ActivationLayerInfo &act_info)
{
ARM_COMPUTE_UNUSED(output);
DataType dt{};
bool append_bias{};
+ bool skip_im2col{};
bool are_weights_reshaped{};
bool is_fully_connected_convolution{};
bool is_interleaved{};
bool is_quantized{};
+ bool is_activationlayer_enabled{};
unsigned int kernel_width = 0;
unsigned int kernel_height = 0;
unsigned int mat_weights_cols = 0;
@@ -494,9 +474,14 @@
unsigned int conv_w = 0;
unsigned int conv_h = 0;
- Status status = validate_and_initialize_values(input, weights, biases, conv_info, weights_info, dt, append_bias, are_weights_reshaped, kernel_width, kernel_height,
- is_fully_connected_convolution, is_interleaved, is_quantized, mat_weights_cols, mat_weights_rows,
- conv_w, conv_h);
+ const DataLayout data_layout = input->data_layout();
+ const bool is_nhwc = data_layout == DataLayout::NHWC;
+ 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);
+
+ Status status = validate_and_initialize_values(input, weights, biases, conv_info, weights_info, act_info, dt, append_bias, skip_im2col, are_weights_reshaped, kernel_width, kernel_height,
+ is_fully_connected_convolution, is_interleaved, is_quantized, is_activationlayer_enabled, mat_weights_cols, mat_weights_rows,
+ conv_w, conv_h, dilation);
const Size2D kernel_weights = Size2D(kernel_width, kernel_height);
@@ -505,68 +490,11 @@
std::unique_ptr<ITensorInfo> reshaped_weights = weights->clone();
bool optimised_kernel = false;
-#if defined(__arm__)
- if(NEScheduler::get().cpu_info().CPU == CPUTarget::ARMV7 && dt == DataType::F32)
+ if(dt == DataType::F32)
{
optimised_kernel = true;
}
-#elif defined(__aarch64__)
- if(NEScheduler::get().cpu_info().CPU >= CPUTarget::ARMV8 && dt == DataType::F32)
- {
- optimised_kernel = true;
- }
-#endif /* defined(__arm__) || defined(__aarch64__) */
- // Reshape weights if needed
- if(optimised_kernel)
- {
- if(are_weights_reshaped)
- {
- mat_weights_cols = weights_info.num_kernels();
- mat_weights_rows = weights->dimension(1);
- }
- else
- {
- TensorShape reshaped_weights_shape{ mat_weights_cols, mat_weights_rows };
-
- // Create tensor to store the reshaped weights
- reshaped_weights->set_tensor_shape(get_reshaped_weights_shape_conv(weights, append_bias, is_fully_connected_convolution));
- ARM_COMPUTE_RETURN_ON_ERROR(NEConvolutionLayerReshapeWeights::validate(weights, biases, reshaped_weights.get(), !is_fully_connected_convolution /* 1xW transpose */));
- weights = reshaped_weights.get();
- }
- }
- else
- {
- if(are_weights_reshaped)
- {
- const unsigned int transpose_width = 16 / input->element_size();
- mat_weights_cols = weights_info.num_kernels();
- mat_weights_rows = weights->dimension(0) / transpose_width + (append_bias ? 1 : 0);
- }
- else
- {
- TensorShape reshaped_weights_shape;
-
- if(is_fully_connected_convolution || is_quantized)
- {
- reshaped_weights_shape = TensorShape{ mat_weights_cols, mat_weights_rows };
- }
- else
- {
- // Create tensor to store transposed weights
- const float transpose_width = 16.0f / input->element_size();
- reshaped_weights_shape = TensorShape{ mat_weights_rows *static_cast<unsigned int>(transpose_width),
- static_cast<unsigned int>(std::ceil(mat_weights_cols / transpose_width)) };
- }
-
- // Create tensor to store the reshaped weights
- reshaped_weights->set_tensor_shape(get_reshaped_weights_shape_conv(weights, append_bias, is_fully_connected_convolution));
- ARM_COMPUTE_RETURN_ON_ERROR(NEConvolutionLayerReshapeWeights::validate(weights, biases, reshaped_weights.get(), !is_fully_connected_convolution /* 1xW transpose */));
- weights = reshaped_weights.get();
- }
- }
-
- // Validate im2col
const unsigned int mat_input_cols = mat_weights_rows;
const unsigned int mat_input_rows = conv_w * conv_h;
TensorShape shape_im2col = input->tensor_shape();
@@ -574,7 +502,17 @@
shape_im2col.set(1, mat_input_rows);
shape_im2col.set(2, 1);
TensorInfo im2_col_info = input->clone()->set_tensor_shape(shape_im2col);
- ARM_COMPUTE_RETURN_ON_ERROR(NEIm2ColKernel::validate(input, &im2_col_info, kernel_weights, conv_info, append_bias, false));
+
+ if(!skip_im2col)
+ {
+ // Validate im2col
+ ARM_COMPUTE_RETURN_ON_ERROR(NEIm2ColKernel::validate(input, &im2_col_info, kernel_weights, conv_info, append_bias, false, false, dilation));
+ }
+ else if(append_bias)
+ {
+ // Validate add bias kernel
+ ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAdditionKernel::validate(output, biases, output, ConvertPolicy::SATURATE));
+ }
// Create GEMM output tensor
TensorShape shape_gemm(im2_col_info.tensor_shape());
@@ -582,19 +520,63 @@
shape_gemm.set(1, mat_input_rows);
TensorInfo gemm_output_info = input->clone()->set_tensor_shape(shape_gemm);
- // Validate GEMM interleave and multiply
- if(is_interleaved)
+ // Reshape weights if needed
+ if(optimised_kernel)
{
- TensorShape shape_interleaved = shape_im2col;
- shape_interleaved.set(0, shape_interleaved.x() * 4);
- shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f));
- TensorInfo input_interleaved_info = input->clone()->set_tensor_shape(shape_interleaved);
- ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMInterleave4x4Kernel::validate(&im2_col_info, &input_interleaved_info));
- ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixMultiplyKernel::validate(&input_interleaved_info, weights, &gemm_output_info, 1.f, is_interleaved, GEMMReshapeInfo()));
+ ARM_COMPUTE_RETURN_ERROR_ON(are_weights_reshaped);
+
+ // Create tensor to store the reshaped weights
+ reshaped_weights->set_tensor_shape(get_reshaped_weights_shape_conv(weights, append_bias, is_fully_connected_convolution));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEConvolutionLayerReshapeWeights::validate(weights, biases, reshaped_weights.get(), !is_fully_connected_convolution /* 1xW transpose */));
}
- else
+ else if(!is_quantized)
{
- ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixMultiplyKernel::validate(&im2_col_info, weights, &gemm_output_info, 1.f, is_interleaved, GEMMReshapeInfo()));
+ TensorShape reshaped_weights_shape;
+
+ if(is_fully_connected_convolution || is_quantized)
+ {
+ reshaped_weights_shape = TensorShape{ mat_weights_cols, mat_weights_rows };
+ }
+ else
+ {
+ // Create tensor to store transposed weights
+ const float transpose_width = 16.0f / input->element_size();
+ reshaped_weights_shape = TensorShape{ mat_weights_rows *static_cast<unsigned int>(transpose_width),
+ static_cast<unsigned int>(std::ceil(mat_weights_cols / transpose_width)) };
+ }
+
+ // Create tensor to store the reshaped weights
+ reshaped_weights->set_tensor_shape(get_reshaped_weights_shape_conv(weights, append_bias, is_fully_connected_convolution));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEConvolutionLayerReshapeWeights::validate(weights, biases, reshaped_weights.get(), !is_fully_connected_convolution /* 1xW transpose */));
+ weights = reshaped_weights.get();
+
+ // Validate GEMM interleave and multiply
+ if(is_interleaved)
+ {
+ TensorShape shape_interleaved = shape_im2col;
+ shape_interleaved.set(idx_width, shape_interleaved.x() * 4);
+ shape_interleaved.set(idx_height, std::ceil(shape_interleaved.y() / 4.f));
+ TensorInfo input_interleaved_info = input->clone()->set_tensor_shape(shape_interleaved);
+ ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMInterleave4x4Kernel::validate(&im2_col_info, &input_interleaved_info));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixMultiplyKernel::validate(&input_interleaved_info, weights, &gemm_output_info, 1.f, is_interleaved, GEMMReshapeInfo(shape_im2col[1], // m
+ weights->tensor_shape()[0], // n
+ shape_im2col[0]) /* k */));
+ }
+ else
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixMultiplyKernel::validate(&im2_col_info, weights, &gemm_output_info, 1.f, is_interleaved, GEMMReshapeInfo()));
+ }
+ }
+ if(!is_nhwc)
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR(NECol2ImKernel::validate(&gemm_output_info, output, Size2D(conv_w, conv_h)));
+ }
+
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG((output->dimension(idx_width) != conv_w) || (output->dimension(idx_height) != conv_h), "Output shape does not match the expected one");
+
+ if(act_info.enabled())
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(output, nullptr, act_info));
}
return Status{};
@@ -605,19 +587,33 @@
// Run weights reshaping (Runs once for every configure)
if(!_are_weights_reshaped)
{
+ ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());
+
_are_weights_reshaped = true;
_reshape_weights.run();
+
+ // Mark original weights tensor as unused
+ _original_weights->mark_as_unused();
}
_memory_group.acquire();
- // Run input reshaping
- NEScheduler::get().schedule(&_input_im2col_kernel, Window::DimY);
+ if(!_skip_im2col)
+ {
+ // Run input reshaping
+ unsigned int _y_dim = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT);
+ NEScheduler::get().schedule(&_input_im2col_kernel, _y_dim);
+ }
// Runs matrix multiply on reshaped matrices
- if(_mm_optimised_kernel != nullptr)
+ if(_asm_glue._optimised_kernel != nullptr)
{
- NEScheduler::get().schedule(_mm_optimised_kernel.get(), Window::DimY);
+ _asm_glue.run();
+ // Release weights in case buffer is pretransposed
+ if(!_weights_reshaped.is_used())
+ {
+ _weights_reshaped.allocator()->free();
+ }
}
else
{
@@ -638,6 +634,11 @@
}
}
+ if(_skip_im2col && _append_bias)
+ {
+ NEScheduler::get().schedule(&_add_bias_kernel, Window::DimY);
+ }
+
// Run output stage for quantized case
if(_is_quantized)
{
@@ -645,7 +646,15 @@
}
// Reshape output matrix
- NEScheduler::get().schedule(&_output_col2im_kernel, Window::DimY);
+ if(_data_layout == DataLayout::NCHW)
+ {
+ NEScheduler::get().schedule(&_output_col2im_kernel, Window::DimY);
+ }
+
+ if(_is_activationlayer_enabled)
+ {
+ _activationlayer_function.run();
+ }
_memory_group.release();
}