COMPMID-1694: Fuse offset contribution with the output stage when we use NEGEMMLowpMatrixMultiplyCore
Change-Id: Ic1a681e4cc03e1eba3bf8485d9cdb17b3e926047
Signed-off-by: giuros01 <giuseppe.rossini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/561
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
diff --git a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
index be7cc2d..b6c3734 100644
--- a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017-2018 ARM Limited.
+ * Copyright (c) 2017-2019 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -90,16 +90,17 @@
}
NEGEMMConvolutionLayer::NEGEMMConvolutionLayer(const 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), _skip_col2im(false), _is_quantized(false), _is_activationlayer_enabled(false), _is_prepared(false)
+ : _memory_group(memory_manager), _reshape_weights(), _im2col_kernel(), _mm_gemm(memory_manager), _mm_gemmlowp(memory_manager), _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),
+ _skip_col2im(false), _is_quantized(false), _is_activationlayer_enabled(false), _is_prepared(false)
{
}
-void NEGEMMConvolutionLayer::configure_mm(const ITensor *input, const ITensor *weights, ITensor *output, int gemm_3d_depth)
+void NEGEMMConvolutionLayer::configure_mm(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const ActivationLayerInfo &act_info, int gemm_3d_depth)
{
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));
+ ARM_COMPUTE_ERROR_THROW_ON(validate_mm(input->info(), weights->info(), biases == nullptr ? nullptr : biases->info(), output == nullptr ? nullptr : output->info(), act_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 */);
@@ -114,7 +115,40 @@
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, nullptr, output, gemm_info);
+ const QuantizationInfo output_quant_info = (output->info()->total_size() == 0) ? input_quantization_info : output->info()->quantization_info();
+
+ float multiplier = input_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);
+
+ // Merge activation with output stage
+ int min_activation = 0;
+ int max_activation = 0;
+
+ const std::set<ActivationLayerInfo::ActivationFunction> supported_acts = { ActivationLayerInfo::ActivationFunction::RELU,
+ ActivationLayerInfo::ActivationFunction::BOUNDED_RELU,
+ ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU
+ };
+ if(_is_activationlayer_enabled && supported_acts.count(act_info.activation()) != 0)
+ {
+ const int a_const_int = output_quant_info.quantize(act_info.a(), RoundingPolicy::TO_NEAREST_UP);
+ const int b_const_int = output_quant_info.quantize(act_info.b(), RoundingPolicy::TO_NEAREST_UP);
+
+ min_activation = act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU ? output_quant_info.offset : b_const_int;
+ max_activation = act_info.activation() == ActivationLayerInfo::ActivationFunction::RELU ? 255 : a_const_int;
+
+ _is_activationlayer_enabled = false;
+ }
+
+ GEMMLowpOutputStageInfo output_info;
+ output_info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT;
+ output_info.gemmlowp_offset = output_quant_info.offset;
+ output_info.gemmlowp_multiplier = output_multiplier;
+ output_info.gemmlowp_shift = output_shift;
+ output_info.gemmlowp_min_bound = min_activation;
+ output_info.gemmlowp_max_bound = max_activation;
+
+ _mm_gemmlowp.configure(input, weights, biases, output, GEMMInfo(false, false, true, gemm_3d_depth, _skip_im2col, false, output_info));
// Revert back QuantizatioInfo as input and weights could be used in other convolution layers
input->info()->set_quantization_info(input_quantization_info);
@@ -127,9 +161,11 @@
}
}
-Status NEGEMMConvolutionLayer::validate_mm(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, int gemm_3d_depth, bool skip_im2col)
+Status NEGEMMConvolutionLayer::validate_mm(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const ActivationLayerInfo &act_info,
+ int gemm_3d_depth, bool skip_im2col)
{
- const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type());
+ const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type());
+ const bool is_activation_enabled = act_info.enabled();
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 */);
@@ -145,8 +181,39 @@
input_qa->set_quantization_info(QuantizationInfo(input_quantization_info.scale, -input_quantization_info.offset));
weights_qa->set_quantization_info(QuantizationInfo(weights_quantization_info.scale, -weights_quantization_info.offset));
+ const QuantizationInfo output_quant_info = (output->total_size() == 0) ? input_quantization_info : output->quantization_info();
+
+ float multiplier = input_quantization_info.scale * weights->quantization_info().scale / output_quant_info.scale;
+ int output_multiplier, output_shift;
+ quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
+
+ // Merge activation with output stage
+ int min_activation = 0;
+ int max_activation = 0;
+
+ const std::set<ActivationLayerInfo::ActivationFunction> supported_acts = { ActivationLayerInfo::ActivationFunction::RELU,
+ ActivationLayerInfo::ActivationFunction::BOUNDED_RELU,
+ ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU
+ };
+ if(is_activation_enabled && supported_acts.count(act_info.activation()) != 0)
+ {
+ const int a_const_int = output_quant_info.quantize(act_info.a(), RoundingPolicy::TO_NEAREST_UP);
+ const int b_const_int = output_quant_info.quantize(act_info.b(), RoundingPolicy::TO_NEAREST_UP);
+
+ min_activation = act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU ? output_quant_info.offset : b_const_int;
+ max_activation = act_info.activation() == ActivationLayerInfo::ActivationFunction::RELU ? 255 : a_const_int;
+ }
+
+ GEMMLowpOutputStageInfo output_info;
+ output_info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT;
+ output_info.gemmlowp_offset = output_quant_info.offset;
+ output_info.gemmlowp_multiplier = output_multiplier;
+ output_info.gemmlowp_shift = output_shift;
+ output_info.gemmlowp_min_bound = min_activation;
+ output_info.gemmlowp_max_bound = max_activation;
+
// Perform validation step on GEMMLowp
- return NEGEMMLowpMatrixMultiplyCore::validate(input_qa.get(), weights_qa.get(), nullptr, output, gemm_info);
+ return NEGEMMLowpMatrixMultiplyCore::validate(input_qa.get(), weights_qa.get(), biases, output, GEMMInfo(false, false, true, gemm_3d_depth, skip_im2col, false, output_info));
}
else
{
@@ -155,19 +222,18 @@
}
}
-Status NEGEMMConvolutionLayer::validate_gemm3d(DataType data_type, int gemm_3d_depth, bool skip_im2col)
+Status NEGEMMConvolutionLayer::validate_gemm3d(const ITensorInfo *input_info, const ActivationLayerInfo &act_info, int gemm_3d_depth, bool skip_im2col)
{
- const bool is_quantized = is_data_type_quantized_asymmetric(data_type);
- const DataType output_gemm_data_type = is_quantized ? DataType::S32 : data_type;
- const unsigned int mult_y = skip_im2col ? 1U : gemm_3d_depth;
- const unsigned int mult_z = skip_im2col ? gemm_3d_depth : 1U;
+ const DataType data_type = input_info->data_type();
+ const unsigned int mult_y = skip_im2col ? 1U : gemm_3d_depth;
+ const unsigned int mult_z = skip_im2col ? gemm_3d_depth : 1U;
// Set dummy tensor shapes for the validation
- const TensorInfo dummy_input_info(TensorShape(4U, 4U * mult_y, 1U * mult_z), 1, data_type);
+ const TensorInfo dummy_input_info(TensorShape(4U, 4U * mult_y, 1U * mult_z), 1, data_type, input_info->quantization_info());
const TensorInfo dummy_weights_info(TensorShape(4U, 4U), 1, data_type);
- const TensorInfo dummy_output_info(TensorShape(4U, 4U, gemm_3d_depth), 1, output_gemm_data_type);
+ const TensorInfo dummy_output_info(TensorShape(4U, 4U, gemm_3d_depth), 1, data_type, input_info->quantization_info());
- return validate_mm(&dummy_input_info, &dummy_weights_info, &dummy_output_info, gemm_3d_depth, skip_im2col);
+ return validate_mm(&dummy_input_info, &dummy_weights_info, nullptr, &dummy_output_info, act_info, gemm_3d_depth, skip_im2col);
}
void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info,
@@ -202,9 +268,8 @@
_append_bias = (biases != nullptr) && (!_is_quantized);
_is_activationlayer_enabled = act_info.enabled();
- const ITensor *gemm_input_to_use = input;
- ITensor *gemm_output_to_use = output;
- ITensor *gemm_output_staged_to_use = output;
+ const ITensor *gemm_input_to_use = input;
+ ITensor *gemm_output_to_use = output;
// Get convolved dimensions
unsigned int conv_w = 0;
@@ -219,7 +284,7 @@
// Check if GEMM3D is supported
if(data_layout == DataLayout::NHWC)
{
- _skip_col2im = bool(validate_gemm3d(input->info()->data_type(), conv_h, true));
+ _skip_col2im = bool(validate_gemm3d(input->info(), act_info, conv_h, true));
// If not supported, we need to perform im2col and col2im (or reshape layer)
if(!_skip_col2im)
{
@@ -262,26 +327,17 @@
}
// Create temporary GEMM output tensor in case we cannot skip col2im
- if(!_skip_col2im || _is_quantized)
+ if(!_skip_col2im)
{
- // 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;
- TensorShape shape_gemm;
+ TensorShape shape_gemm;
- if(_is_quantized && _skip_col2im)
- {
- shape_gemm = output->info()->tensor_shape();
- }
- else
- {
- // Calculate GEMM output shape
- shape_gemm = _im2col_output.info()->tensor_shape();
- shape_gemm.set(0, mat_weights_cols);
- shape_gemm.set(1, conv_w * conv_h);
- }
+ // Calculate GEMM output shape
+ shape_gemm = _im2col_output.info()->tensor_shape();
+ shape_gemm.set(0, mat_weights_cols);
+ shape_gemm.set(1, conv_w * conv_h);
// FIXME: input->clone() doesn't work with subtensors for grouped convolutions.
- TensorInfo info_gemm(shape_gemm, 1, gemm_data_type);
+ TensorInfo info_gemm(shape_gemm, 1, data_type);
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);
@@ -293,62 +349,24 @@
// Configure GEMM
// In case we need to skip col2im, GEMM3D (gemm_3d_depth != 0) must be called in order to avoid reshaping the output matrix
const unsigned int gemm_3d_depth = _skip_col2im ? conv_h : 0;
- configure_mm(gemm_input_to_use, &_weights_reshaped, gemm_output_to_use, gemm_3d_depth);
+ configure_mm(gemm_input_to_use, &_weights_reshaped, biases, gemm_output_to_use, act_info, gemm_3d_depth);
if(!_skip_im2col)
{
_im2col_output.allocator()->allocate();
}
- // Configure output stage for quantized case
- if(_is_quantized)
- {
- const QuantizationInfo input_quant_info = input->info()->quantization_info();
- const QuantizationInfo output_quant_info = (output->info()->total_size() == 0) ? input_quant_info : output->info()->quantization_info();
-
- float multiplier = input_quant_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);
-
- if(!_skip_col2im)
- {
- _memory_group.manage(&_tmp_output);
- gemm_output_staged_to_use = &_tmp_output;
- }
-
- // Merge activation with output stage
- int min_activation = 0;
- int max_activation = 0;
-
- const std::set<ActivationLayerInfo::ActivationFunction> supported_acts = { ActivationLayerInfo::ActivationFunction::RELU,
- ActivationLayerInfo::ActivationFunction::BOUNDED_RELU,
- ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU
- };
- if(_is_activationlayer_enabled && supported_acts.count(act_info.activation()) != 0)
- {
- const int a_const_int = output_quant_info.quantize(act_info.a(), RoundingPolicy::TO_NEAREST_UP);
- const int b_const_int = output_quant_info.quantize(act_info.b(), RoundingPolicy::TO_NEAREST_UP);
-
- min_activation = act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU ? output_quant_info.offset : b_const_int;
- max_activation = act_info.activation() == ActivationLayerInfo::ActivationFunction::RELU ? 255 : a_const_int;
-
- _is_activationlayer_enabled = false;
- }
-
- _gemmlowp_output_stage.configure(gemm_output_to_use, biases, gemm_output_staged_to_use, output_multiplier, output_shift, output_quant_info.offset, min_activation, max_activation);
- }
-
if(!_skip_col2im)
{
if(_data_layout == DataLayout::NCHW)
{
// Configure col2im
- _col2im_kernel.configure(_is_quantized ? gemm_output_staged_to_use : gemm_output_to_use, output, Size2D(conv_w, conv_h));
+ _col2im_kernel.configure(gemm_output_to_use, output, Size2D(conv_w, conv_h));
}
else
{
// Configure reshape layer
- _reshape_layer.configure(_is_quantized ? gemm_output_staged_to_use : gemm_output_to_use, output);
+ _reshape_layer.configure(gemm_output_to_use, output);
}
}
@@ -395,10 +413,9 @@
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 ITensorInfo *gemm_input_to_use = input;
+ const ITensorInfo *gemm_output_to_use = output;
+ const ITensorInfo *weights_to_use = weights;
const bool is_quantized = is_data_type_quantized_asymmetric(data_type);
const bool append_bias = (biases != nullptr) && (!is_quantized);
@@ -420,7 +437,7 @@
bool skip_col2im = false;
if(data_layout == DataLayout::NHWC)
{
- skip_col2im = bool(validate_gemm3d(input->data_type(), conv_h, true));
+ skip_col2im = bool(validate_gemm3d(input, act_info, conv_h, true));
// If not supported, we need to perform im2col and col2im (or reshape layer)
if(!skip_col2im)
{
@@ -431,7 +448,7 @@
if(skip_col2im)
{
// If not supported, we need to perform im2col and col2im (or reshape layer)
- if(!bool(validate_gemm3d(input->data_type(), conv_h, skip_im2col)))
+ if(!bool(validate_gemm3d(input, act_info, conv_h, skip_im2col)))
{
skip_im2col = false;
skip_col2im = false;
@@ -495,68 +512,25 @@
}
// Create temporary GEMM output tensor in case we cannot skip col2im
- const DataType gemm_data_type = is_quantized ? DataType::S32 : data_type;
if(!skip_col2im)
{
TensorShape shape_gemm = gemm_input_to_use->tensor_shape();
shape_gemm.set(0, mat_weights_cols);
shape_gemm.set(1, conv_w * conv_h);
- info_gemm = TensorInfo(shape_gemm, 1, gemm_data_type);
+ info_gemm = TensorInfo(shape_gemm, 1, data_type);
}
else
{
- info_gemm = TensorInfo(output->tensor_shape(), 1, gemm_data_type);
+ info_gemm = TensorInfo(output->tensor_shape(), 1, data_type);
}
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, skip_col2im ? conv_h : 0, skip_im2col));
-
- if(is_quantized)
- {
- const QuantizationInfo input_quant_info = input->quantization_info();
- const QuantizationInfo output_quant_info = (output->total_size() == 0) ? input_quant_info : output->quantization_info();
- const float multiplier = input_quant_info.scale * weights_to_use->quantization_info().scale / output_quant_info.scale;
- int output_multiplier, output_shift;
- quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
-
- if(!skip_col2im)
- {
- tmp_info = TensorInfo(gemm_output_to_use->tensor_shape(), 1, DataType::QASYMM8);
- tmp_info.set_quantization_info(output->quantization_info()).set_data_layout(data_layout);
- gemm_output_staged_to_use = &tmp_info;
- }
-
- // Merge activation with output stage
- int min_activation = 0;
- int max_activation = 0;
-
- const std::set<ActivationLayerInfo::ActivationFunction> supported_acts = { ActivationLayerInfo::ActivationFunction::RELU,
- ActivationLayerInfo::ActivationFunction::BOUNDED_RELU,
- ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU
- };
-
- if(is_activation_enabled && supported_acts.count(act_info.activation()) != 0)
- {
- const int a_const_int = output_quant_info.quantize(act_info.a(), RoundingPolicy::TO_NEAREST_UP);
- const int b_const_int = output_quant_info.quantize(act_info.b(), RoundingPolicy::TO_NEAREST_UP);
-
- min_activation = act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU ? output_quant_info.offset : b_const_int;
- max_activation = act_info.activation() == ActivationLayerInfo::ActivationFunction::RELU ? 255 : a_const_int;
-
- is_activation_enabled = false;
- }
-
- // Validate output stage for quantized case
- NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(gemm_output_to_use, biases, gemm_output_staged_to_use, min_activation, max_activation);
- }
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemm_input_to_use, weights_to_use, biases, gemm_output_to_use, act_info, skip_col2im ? conv_h : 0, skip_im2col));
// Validate Col2Im/ReshapeLayer
if(!skip_col2im && (data_layout == DataLayout::NCHW))
{
- ARM_COMPUTE_RETURN_ON_ERROR(NECol2ImKernel::validate(is_quantized ? gemm_output_staged_to_use : gemm_output_to_use,
- output,
- Size2D(conv_w, conv_h)));
+ ARM_COMPUTE_RETURN_ON_ERROR(NECol2ImKernel::validate(gemm_output_to_use, output, Size2D(conv_w, conv_h)));
}
//Validate Activation Layer
@@ -586,9 +560,6 @@
{
// Run gemmlowp
_mm_gemmlowp.run();
-
- // Run output stage
- _gemmlowp_output_stage.run();
}
else
{