arm_compute v18.01
Change-Id: I9bfa178c2e38bfd5fc812e62aab6760d87748e05
diff --git a/src/runtime/CL/functions/CLConvolutionLayer.cpp b/src/runtime/CL/functions/CLConvolutionLayer.cpp
index 0ed3351..b3af11e 100644
--- a/src/runtime/CL/functions/CLConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLConvolutionLayer.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -43,9 +43,6 @@
void CLConvolutionLayerReshapeWeights::configure(const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, bool transpose1xW)
{
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(weights, output);
ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);
if(biases != nullptr)
@@ -82,13 +79,14 @@
{
_weights_reshape_kernel.configure(weights, biases_to_use, output);
}
+
+ output->info()->set_quantization_info(weights->info()->quantization_info());
}
void CLConvolutionLayerReshapeWeights::run()
{
_memory_group.acquire();
- cl::CommandQueue q = CLScheduler::get().queue();
CLScheduler::get().enqueue(_weights_reshape_kernel);
if(_transpose1xW)
{
@@ -99,33 +97,49 @@
}
CLConvolutionLayer::CLConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
- : _memory_group(memory_manager), _reshape_weights(), _input_im2col_kernel(), _input_interleave_kernel(), _mm_kernel(), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(), _output_col2im_kernel(),
- _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _weights_transposed(), _gemm_output(), _tmp_output(), _append_bias(false), _is_fully_connected_convolution(false),
- _are_weights_reshaped(false), _is_quantized(false)
+ : _memory_group(memory_manager), _reshape_weights(), _im2col_kernel(), _interleave_kernel(), _mm_kernel(), _mm_gemm(memory_manager), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(),
+ _col2im_kernel(), _im2col_output(), _interleave_output(), _weights_reshaped(), _weights_transposed(), _gemm_output(), _tmp_output(), _are_weights_reshaped(false), _is_quantized(false),
+ _is_interleaved_transposed(false)
{
}
-void CLConvolutionLayer::configure_mm(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output, bool is_interleaved_transposed)
+void CLConvolutionLayer::configure_mm(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output, bool is_interleaved_transposed, bool are_weights_reshaped)
{
if(_is_quantized)
{
- // 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->info()->quantization_info();
- const QuantizationInfo weights_quantization_info = weights->info()->quantization_info();
+ if(are_weights_reshaped)
+ {
+ ARM_COMPUTE_ERROR("Weights already reshaped are not suppported with gemmlowp");
+ }
+ else
+ {
+ // 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->info()->quantization_info();
+ const QuantizationInfo weights_quantization_info = weights->info()->quantization_info();
- 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));
+ 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, GEMMInfo(false, false, true /* Reshape weights only for the first run*/));
- // Revert back QuantizatioInfo as input and weights could be used in other convolution layers
- input->info()->set_quantization_info(input_quantization_info);
- weights->info()->set_quantization_info(weights_quantization_info);
+ // Revert back QuantizatioInfo as input and weights could be used in other convolution layers
+ input->info()->set_quantization_info(input_quantization_info);
+ weights->info()->set_quantization_info(weights_quantization_info);
+ }
}
else
{
- _mm_kernel.configure(input, weights, output, 1.f, is_interleaved_transposed);
+ if(are_weights_reshaped)
+ {
+ // Configure matrix multiply kernel
+ _mm_kernel.configure(input, weights, output, 1.f, is_interleaved_transposed);
+ }
+ 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*/));
+ }
}
}
@@ -134,6 +148,7 @@
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, weights);
+ ARM_COMPUTE_ERROR_ON(weights_info.are_reshaped() && CLScheduler::get().target() == GPUTarget::BIFROST);
ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && weights->info()->dimension(2) != input->info()->dimension(2));
ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);
ARM_COMPUTE_ERROR_ON(weights_info.are_reshaped() && is_data_type_quantized_asymmetric(input->info()->data_type()));
@@ -157,14 +172,16 @@
const DataType dt = input->info()->data_type();
- // Set the GPU target for matrix multiply
+ // Set the GPU target for matrix multiply and im2col and col2im
_mm_kernel.set_target(CLScheduler::get().target());
+ _im2col_kernel.set_target(CLScheduler::get().target());
+ _col2im_kernel.set_target(CLScheduler::get().target());
- _append_bias = (biases != nullptr) && (!_is_quantized);
- _are_weights_reshaped = weights_info.are_reshaped();
+ const bool append_bias = (biases != nullptr) && (!_is_quantized);
+ _are_weights_reshaped = weights_info.are_reshaped();
- const unsigned bias_element = (_append_bias) ? 1 : 0;
- const ICLTensor *biases_to_use = (_append_bias) ? biases : nullptr;
+ const unsigned bias_element = (append_bias) ? 1 : 0;
+ const ICLTensor *biases_to_use = (append_bias) ? biases : nullptr;
// Get parameters from conv_info
unsigned int stride_x = 0;
@@ -181,8 +198,8 @@
conv_info);
// Check if its a "fully connected" convolution
- _is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1));
- const bool run_interleaved = (!_is_fully_connected_convolution && !_is_quantized);
+ const bool is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1));
+ _is_interleaved_transposed = (!is_fully_connected_convolution) && (!_is_quantized) && (_are_weights_reshaped);
unsigned int mat_weights_cols = weights->info()->dimension(3);
unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + bias_element;
@@ -190,7 +207,7 @@
// Reshape weights if needed
if(_are_weights_reshaped)
{
- if(_is_fully_connected_convolution || _is_quantized)
+ if(is_fully_connected_convolution || _is_quantized)
{
mat_weights_cols = weights->info()->dimension(0);
mat_weights_rows = weights->info()->dimension(1);
@@ -204,22 +221,10 @@
}
else
{
- if(_is_fully_connected_convolution || _is_quantized)
- {
- // Create tensor to store the reshaped weights
- TensorShape shape_wr(mat_weights_cols, mat_weights_rows);
- _weights_reshaped.allocator()->init(weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_wr));
- _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, false /* 1xW transpose */);
- }
- else
- {
- // Create tensor to store transposed weights
- const float transpose_width = 16.0f / input->info()->element_size();
- TensorShape shape_wt(mat_weights_rows * static_cast<unsigned int>(transpose_width), static_cast<unsigned int>(std::ceil(mat_weights_cols / transpose_width)));
- _weights_reshaped.allocator()->init(weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_wt));
- _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, true /* 1xW transpose */);
- }
- _weights_reshaped.info()->set_quantization_info(weights->info()->quantization_info());
+ // _weights_reshaped will be auto configured in the kernel.
+ // Just append biases and do not transpose 1xW as it will be reshaped in CLGEMM
+ _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, false);
+
weights = &_weights_reshaped;
}
@@ -230,50 +235,43 @@
shape_im2col.set(0, mat_input_cols);
shape_im2col.set(1, mat_input_rows);
shape_im2col.set(2, 1);
+ //input->clone() doesn't work with subtensors for grouped convolutions.
TensorInfo im2col_reshaped_info(shape_im2col, 1, dt, input->info()->fixed_point_position());
im2col_reshaped_info.set_quantization_info(input->info()->quantization_info());
- _input_im2col_reshaped.allocator()->init(im2col_reshaped_info);
- _memory_group.manage(&_input_im2col_reshaped);
-
- // Create tensor (interleave) to prepare input tensor for GEMM
- if(run_interleaved)
- {
- 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 interleaved_info(shape_interleaved, 1, dt, input->info()->fixed_point_position());
- interleaved_info.set_quantization_info(input->info()->quantization_info());
- _input_interleaved_reshaped.allocator()->init(interleaved_info);
- _memory_group.manage(&_input_interleaved_reshaped);
- }
+ _im2col_output.allocator()->init(im2col_reshaped_info);
+ _memory_group.manage(&_im2col_output);
// Create GEMM output tensor
- TensorShape shape_gemm = _input_im2col_reshaped.info()->tensor_shape();
+ TensorShape shape_gemm = _im2col_output.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.
+ //input->clone() doesn't work with subtensors for grouped convolutions.
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
- _input_im2col_kernel.set_target(CLScheduler::get().target());
- _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _append_bias);
+ // Configure im2col
+ _im2col_kernel.configure(input, &_im2col_output, Size2D(kernel_width, kernel_height), conv_info, append_bias);
// Configure matrix multiply
- if(run_interleaved)
+ if(_is_interleaved_transposed)
{
- _input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped);
- configure_mm(&_input_interleaved_reshaped, weights, &_gemm_output);
- _input_interleaved_reshaped.allocator()->allocate();
+ // Configure GEMMInterleave4x4. _input_interleaved_reshaped will be auto configured in the kernel
+ _interleave_kernel.configure(&_im2col_output, &_interleave_output);
+ _memory_group.manage(&_interleave_output);
+
+ // Configure GEMM
+ configure_mm(&_interleave_output, weights, &_gemm_output, true, _are_weights_reshaped);
+ _interleave_output.allocator()->allocate();
}
else
{
- configure_mm(&_input_im2col_reshaped, weights, &_gemm_output, false);
+ configure_mm(&_im2col_output, weights, &_gemm_output, false, _are_weights_reshaped);
}
- _input_im2col_reshaped.allocator()->allocate();
+ _im2col_output.allocator()->allocate();
// Configure output stage for quantized case
if(_is_quantized)
@@ -286,8 +284,7 @@
}
// Configure Col2Im
- _output_col2im_kernel.set_target(CLScheduler::get().target());
- _output_col2im_kernel.configure(_is_quantized ? &_tmp_output : &_gemm_output, output, std::make_pair(conv_w, conv_h));
+ _col2im_kernel.configure(_is_quantized ? &_tmp_output : &_gemm_output, output, std::make_pair(conv_w, conv_h));
if(_is_quantized)
{
_tmp_output.allocator()->allocate();
@@ -318,32 +315,39 @@
_memory_group.acquire();
// Run im2col
- CLScheduler::get().enqueue(_input_im2col_kernel);
+ CLScheduler::get().enqueue(_im2col_kernel);
- if(!_is_fully_connected_convolution && !_is_quantized)
+ // Note: _is_interleaved_transposed is true only if the weights passed to the function have been passed already reshaped
+ // and if we do not have QASYMM8 data type. If this flag is true, we need to run the
+ // gemm kernel instead of gemm function
+ if(_is_interleaved_transposed)
{
- // Run interleave4x4
- CLScheduler::get().enqueue(_input_interleave_kernel);
- }
+ // Run interleave4x4 kernel
+ CLScheduler::get().enqueue(_interleave_kernel);
- // Runs matrix multiply on reshaped matrices
- if(_is_quantized)
- {
- _mm_gemmlowp.run();
+ // Run matrix multiply kernel
+ CLScheduler::get().enqueue(_mm_kernel);
}
else
{
- CLScheduler::get().enqueue(_mm_kernel);
- }
+ // Runs CLGEMM or CLGEMMLowpMatrixMultiplyCore functions
+ if(_is_quantized)
+ {
+ // Run gemmlowp
+ _mm_gemmlowp.run();
- // Run output stage for quantized case
- if(_is_quantized)
- {
- _gemmlowp_output_stage.run();
+ // Run output stage
+ _gemmlowp_output_stage.run();
+ }
+ else
+ {
+ // Run gemm
+ _mm_gemm.run();
+ }
}
// Reshape output matrix
- CLScheduler::get().enqueue(_output_col2im_kernel, false);
+ CLScheduler::get().enqueue(_col2im_kernel, false);
_memory_group.release();
}