arm_compute v18.08
diff --git a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
index 79495e4..92d04d6 100644
--- a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
@@ -43,42 +43,43 @@
{
}
-void CLConvolutionLayerReshapeWeights::configure(const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output)
+void CLConvolutionLayerReshapeWeights::configure(const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, unsigned int num_groups)
{
// Perform validation step
ARM_COMPUTE_ERROR_ON_NULLPTR(weights, output);
ARM_COMPUTE_ERROR_THROW_ON(CLConvolutionLayerReshapeWeights::validate(weights->info(),
(biases != nullptr) ? biases->info() : nullptr,
- output->info()));
+ output->info(),
+ num_groups));
const bool append_biases = (biases != nullptr) && !is_data_type_quantized_asymmetric(weights->info()->data_type());
const ICLTensor *biases_to_use = (append_biases) ? biases : nullptr;
- _weights_reshape_kernel.configure(weights, biases_to_use, output);
+ _weights_reshape_kernel.configure(weights, biases_to_use, output, num_groups);
output->info()->set_quantization_info(weights->info()->quantization_info());
}
-Status CLConvolutionLayerReshapeWeights::validate(const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output)
+Status CLConvolutionLayerReshapeWeights::validate(const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, unsigned int num_groups)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(weights);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
if(biases != nullptr)
{
+ const int idx_kernels = get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::BATCHES);
ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_quantized_asymmetric(weights->data_type()));
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
- ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3));
+ ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(idx_kernels));
ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
}
if((output != nullptr) && (output->total_size() != 0))
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(weights, output);
- CLWeightsReshapeKernel::validate(weights, biases, output);
+ CLWeightsReshapeKernel::validate(weights, biases, output, num_groups);
}
return Status{};
@@ -91,14 +92,15 @@
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(),
- _original_weights(nullptr), _im2col_output(), _weights_reshaped(), _gemm_output(), _tmp_output(), _is_quantized(false), _is_activationlayer_enabled(false), _is_prepared(false)
+ _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)
{
}
-void CLGEMMConvolutionLayer::configure_mm(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output)
+void CLGEMMConvolutionLayer::configure_mm(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output, int gemm_3d_depth)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights);
- ARM_COMPUTE_ERROR_THROW_ON(validate_mm(input->info(), weights->info(), output->info()));
+ ARM_COMPUTE_ERROR_THROW_ON(validate_mm(input->info(), weights->info(), output->info(), gemm_3d_depth, _skip_im2col));
if(_is_quantized)
{
@@ -119,15 +121,16 @@
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*/));
+ _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 */));
}
}
-Status CLGEMMConvolutionLayer::validate_mm(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output)
+Status CLGEMMConvolutionLayer::validate_mm(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, int gemm_3d_depth, bool skip_im2col)
{
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 */);
+ 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()
@@ -141,18 +144,17 @@
weights_qa->set_quantization_info(QuantizationInfo(weights_quantization_info.scale, -weights_quantization_info.offset));
// Perform validation step on GEMMLowp
- CLGEMMLowpMatrixMultiplyCore::validate(input_qa.get(), weights_qa.get(), output, gemm_info);
+ return CLGEMMLowpMatrixMultiplyCore::validate(input_qa.get(), weights_qa.get(), output, gemm_info);
}
else
{
// Perform validation step on Matrix multiply function
- CLGEMM::validate(input, weights, nullptr, output, 1.0f, 0.0f, gemm_info);
+ return CLGEMM::validate(input, weights, nullptr, output, 1.0f, 0.0f, gemm_info);
}
- return Status{};
}
void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info,
- const Size2D &dilation, const ActivationLayerInfo &act_info)
+ const Size2D &dilation, const ActivationLayerInfo &act_info, unsigned int num_groups)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
@@ -163,22 +165,35 @@
conv_info,
weights_info,
dilation,
- act_info));
+ act_info,
+ num_groups));
- _is_prepared = false;
+ const DataType data_type = input->info()->data_type();
+ 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_kernels = get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES);
+
+ const unsigned int kernel_width = weights->info()->dimension(idx_width);
+ const unsigned int kernel_height = weights->info()->dimension(idx_height);
+
+ _is_prepared = weights_info.retain_internal_weights();
_original_weights = weights;
_is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type());
-
- const DataType dt = 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;
+ _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());
- const bool append_bias = (biases != nullptr) && (!_is_quantized);
+ 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;
- const unsigned bias_element = (append_bias) ? 1 : 0;
- const ICLTensor *biases_to_use = (append_bias) ? biases : nullptr;
+ const ICLTensor *biases_to_use = (_append_bias && !_skip_im2col) ? biases : nullptr;
// Get parameters from conv_info
unsigned int stride_x = 0;
@@ -188,51 +203,66 @@
// Get convolved dimensions
unsigned int conv_w = 0;
unsigned int conv_h = 0;
+ std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(idx_width),
+ input->info()->dimension(idx_height),
+ kernel_width,
+ kernel_height,
+ conv_info,
+ dilation);
- const unsigned int kernel_width = weights->info()->dimension(0);
- const unsigned int kernel_height = weights->info()->dimension(1);
- std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), kernel_width, kernel_height,
- conv_info, dilation);
-
- 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;
+ unsigned int mat_weights_cols = weights->info()->dimension(idx_kernels) / num_groups;
// _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);
-
- weights = &_weights_reshaped;
+ _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, num_groups);
// 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);
- TensorInfo im2col_reshaped_info(shape_im2col, 1, dt, input->info()->fixed_point_position());
- im2col_reshaped_info.set_quantization_info(input->info()->quantization_info());
- _im2col_output.allocator()->init(im2col_reshaped_info);
- _memory_group.manage(&_im2col_output);
+ if(!_skip_im2col)
+ {
+ _memory_group.manage(&_im2col_output);
+
+ // Configure and tune im2col. im2col output shape is auto-initialized
+ _im2col_kernel.configure(input, &_im2col_output, Size2D(kernel_width, kernel_height), conv_info, _append_bias, dilation, num_groups);
+
+ // Set quantization info
+ _im2col_output.info()->set_quantization_info(input->info()->quantization_info());
+ CLScheduler::get().tune_kernel_static(_im2col_kernel);
+
+ // Update GEMM input
+ gemm_input_to_use = &_im2col_output;
+ }
+ else if(_append_bias)
+ {
+ // Configure add bias kernel
+ _add_bias_kernel.configure(output, biases, output, ConvertPolicy::SATURATE);
+ }
// Create GEMM output tensor
- 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.
- 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);
+ if(!is_nhwc || _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);
- // Configure im2col
- _im2col_kernel.configure(input, &_im2col_output, Size2D(kernel_width, kernel_height), conv_info, append_bias, dilation);
+ // 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;
+ TensorInfo info_gemm(shape_gemm, 1, gemm_data_type);
+ info_gemm.set_quantization_info(output->info()->quantization_info());
+ _gemm_output.allocator()->init(info_gemm);
+ _memory_group.manage(&_gemm_output);
- // Configure GEMM
- configure_mm(&_im2col_output, weights, &_gemm_output);
+ // Update GEMM output
+ gemm_output_to_use = &_gemm_output;
+ }
- _im2col_output.allocator()->allocate();
+ // Configure and tune GEMM
+ configure_mm(gemm_input_to_use, &_weights_reshaped, gemm_output_to_use, (data_layout == DataLayout::NHWC) ? conv_h : 1);
+
+ if(!_skip_im2col)
+ {
+ _im2col_output.allocator()->allocate();
+ }
// Configure output stage for quantized case
if(_is_quantized)
@@ -242,19 +272,36 @@
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);
+ 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);
}
- // Configure Col2Im
- _col2im_kernel.configure(_is_quantized ? &_tmp_output : &_gemm_output, output, std::make_pair(conv_w, conv_h));
- if(_is_quantized)
+ if(!is_nhwc || _is_quantized)
+ {
+ 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, std::make_pair(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);
+ }
+ }
+
+ if(!is_nhwc || _is_quantized)
{
_tmp_output.allocator()->allocate();
+ _gemm_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");
//Configure Activation Layer
_is_activationlayer_enabled = act_info.enabled();
@@ -268,76 +315,42 @@
}
Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
- const WeightsInfo &weights_info, const Size2D &dilation, const ActivationLayerInfo &act_info)
+ const WeightsInfo &weights_info, const Size2D &dilation, const ActivationLayerInfo &act_info, unsigned int num_groups)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights_info.are_reshaped(), "Weights already reshaped are not supported!");
- 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_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, 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->dimension(2) != input->dimension(2));
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, weights);
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG((num_groups != 1) && (input->data_layout() != DataLayout::NCHW), "Grouping (num_groups != 1) with NHWC data layout is not supported");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG((num_groups != 1) && (input->data_type() == DataType::QASYMM8), "Grouping (num_groups != 1) is not supported with QASYMM8");
+ ARM_COMPUTE_RETURN_ERROR_ON(((input->dimension(2) / weights->dimension(2)) != num_groups) && (input->data_layout() == DataLayout::NCHW));
+
+ const DataLayout data_layout = input->data_layout();
+ const DataType data_type = input->data_type();
+ 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->dimension(idx_width);
+ const unsigned int kernel_height = weights->dimension(idx_height);
+
+ TensorInfo im2col_reshaped_info, info_gemm, tmp_info, weights_reshaped_info;
+ const ITensorInfo *gemm_input_to_use = input;
+ const ITensorInfo *gemm_output_to_use = output;
+ 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);
+
+ ARM_COMPUTE_RETURN_ERROR_ON((weights->dimension(idx_channel) * num_groups) != input->dimension(idx_channel));
ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
- if(act_info.enabled())
- {
- ARM_COMPUTE_ERROR_ON(act_info.b() > act_info.a());
- }
-
- const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type());
- const bool append_bias = (biases != nullptr) && (!is_quantized);
- const unsigned bias_element = (append_bias) ? 1 : 0;
- const DataType dt = input->data_type();
-
- // Get convolved dimensions
- unsigned int conv_w = 0;
- unsigned int conv_h = 0;
-
- const unsigned int kernel_width = weights->dimension(0);
- const unsigned int kernel_height = weights->dimension(1);
-
- std::tie(conv_w, conv_h) = scaled_dimensions(input->dimension(0), input->dimension(1), kernel_width, kernel_height, conv_info, dilation);
-
- unsigned int mat_weights_cols = weights->dimension(3);
- unsigned int mat_weights_rows = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + bias_element;
-
- ARM_COMPUTE_RETURN_ON_ERROR(CLConvolutionLayerReshapeWeights::validate(weights, is_quantized ? nullptr : biases, nullptr));
-
- // Create tensor info for 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->tensor_shape();
- shape_im2col.set(0, mat_input_cols);
- shape_im2col.set(1, mat_input_rows);
- shape_im2col.set(2, 1);
- TensorInfo im2col_reshaped_info(shape_im2col, 1, dt, input->fixed_point_position());
- im2col_reshaped_info.set_quantization_info(input->quantization_info());
- ARM_COMPUTE_RETURN_ON_ERROR(CLIm2ColKernel::validate(input, &im2col_reshaped_info, Size2D(kernel_width, kernel_height), conv_info, append_bias, dilation));
-
- // Create GEMM output tensor
- TensorShape shape_gemm = 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->fixed_point_position());
- info_gemm.set_quantization_info(output->quantization_info());
-
- ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(&im2col_reshaped_info, weights, &info_gemm));
- TensorInfo tmp_info(shape_gemm, 1, DataType::QASYMM8, input->fixed_point_position());
- tmp_info.set_quantization_info(output->quantization_info());
-
- if(is_quantized)
- {
- float multiplier = input->quantization_info().scale * weights->quantization_info().scale / output->quantization_info().scale;
- int output_multiplier, output_shift;
- quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
- // Validate output stage for quantized case
- CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(&info_gemm, biases, &tmp_info, output->quantization_info().offset);
- }
-
- // Validate Col2Im
- ARM_COMPUTE_RETURN_ON_ERROR(CLCol2ImKernel::validate(is_quantized ? &tmp_info : &info_gemm, output, std::make_pair(conv_w, conv_h)));
-
+ // Validate biases
if(biases != nullptr)
{
if(is_quantized)
@@ -348,11 +361,91 @@
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
}
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, biases);
- ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3));
+ ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(idx_kernels));
ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
}
+ if(act_info.enabled())
+ {
+ ARM_COMPUTE_ERROR_ON(act_info.b() > act_info.a());
+ }
+
+ // Get convolved dimensions
+ unsigned int conv_w = 0;
+ unsigned int conv_h = 0;
+
+ std::tie(conv_w, conv_h) = scaled_dimensions(input->dimension(idx_width),
+ input->dimension(idx_height),
+ kernel_width,
+ kernel_height,
+ conv_info,
+ dilation);
+
+ unsigned int mat_weights_cols = weights->dimension(idx_kernels) / num_groups;
+
+ // Output tensor auto inizialitation if not yet initialized
+ ARM_COMPUTE_RETURN_ON_ERROR(CLConvolutionLayerReshapeWeights::validate(weights, is_quantized ? nullptr : biases, nullptr, num_groups));
+ weights_reshaped_info = TensorInfo(compute_weights_reshaped_shape(*weights, (append_bias && !skip_im2col), num_groups), 1, data_type);
+ weights_to_use = &weights_reshaped_info;
+
+ if(!skip_im2col)
+ {
+ const Size2D kernel_dims(kernel_width, kernel_height);
+
+ // Output tensor auto initialization if not yet initialized
+ TensorShape expected_output_shape = compute_im2col_conv_shape(input, kernel_dims, conv_info, append_bias, dilation, num_groups == 1, num_groups);
+
+ auto_init_if_empty(im2col_reshaped_info, input->clone()->set_tensor_shape(expected_output_shape));
+
+ ARM_COMPUTE_RETURN_ON_ERROR(CLIm2ColKernel::validate(input, &im2col_reshaped_info, kernel_dims, conv_info, append_bias, dilation, num_groups));
+ gemm_input_to_use = &im2col_reshaped_info;
+ }
+ else if(append_bias)
+ {
+ // Validate add bias kernel
+ ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAdditionKernel::validate(output, biases, output, ConvertPolicy::SATURATE));
+ }
+
+ // Create GEMM output tensor
+ if(!is_nhwc || 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;
+ // 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());
+ 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));
+
+ if(is_quantized)
+ {
+ float multiplier = input->quantization_info().scale * weights_to_use->quantization_info().scale / output->quantization_info().scale;
+ 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;
+
+ // Validate output stage for quantized case
+ CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(gemm_output_to_use, biases, gemm_output_staged_to_use, output->quantization_info().offset);
+ }
+
+ // Validate Col2Im
+ if(!is_nhwc || is_quantized)
+ {
+ 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,
+ std::make_pair(conv_w, conv_h), num_groups));
+ }
+ }
+
//Validate Activation Layer
if(act_info.enabled())
{
@@ -369,7 +462,10 @@
_memory_group.acquire();
// Run im2col
- CLScheduler::get().enqueue(_im2col_kernel);
+ if(!_skip_im2col)
+ {
+ CLScheduler::get().enqueue(_im2col_kernel);
+ }
// Runs CLGEMM or CLGEMMLowpMatrixMultiplyCore functions
if(_is_quantized)
@@ -386,8 +482,23 @@
_mm_gemm.run();
}
+ if(_skip_im2col && _append_bias)
+ {
+ CLScheduler::get().enqueue(_add_bias_kernel);
+ }
+
// Reshape output matrix
- CLScheduler::get().enqueue(_col2im_kernel, false);
+ if(_data_layout == DataLayout::NCHW || _is_quantized)
+ {
+ if(_data_layout == DataLayout::NCHW)
+ {
+ CLScheduler::get().enqueue(_col2im_kernel, false);
+ }
+ else
+ {
+ _reshape_layer.run();
+ }
+ }
//Run Activation Layer if enabled
if(_is_activationlayer_enabled)
@@ -402,20 +513,18 @@
{
if(!_is_prepared)
{
- // Run weights reshaping and mark as unused
ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());
+
+ // Run weights reshaping and mark original weights tensor as unused
_weights_reshaped.allocator()->allocate();
_reshape_weights.run();
_original_weights->mark_as_unused();
- // Run GEMM prepare
- if(!_is_quantized)
+ // Prepare GEMM
+ _is_quantized ? _mm_gemmlowp.prepare() : _mm_gemm.prepare();
+ if(!_weights_reshaped.is_used())
{
- _mm_gemm.prepare();
- if(!_weights_reshaped.is_used())
- {
- _weights_reshaped.allocator()->free();
- }
+ _weights_reshaped.allocator()->free();
}
CLScheduler::get().queue().finish();