COMPMID-2306: CLDepthwiseConvolution: support for QUANT8_PER_CHANNEL_SYMM
Change-Id: I18c886400daa2dcba0b91011bc4e503d807a4732
Signed-off-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-on: https://review.mlplatform.org/c/2143
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Giorgio Arena <giorgio.arena@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
diff --git a/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp b/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp
index 168d7d5..cdf3a95 100644
--- a/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp
@@ -45,6 +45,7 @@
{
// This function should be removed and incorporated inside CLDepthwiseConvolutionLayerInternal3x3 once CLDepthwiseConvolutionLayer3x3 is properly removed
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() == DataLayout::UNKNOWN);
const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type());
@@ -58,15 +59,20 @@
info.c0 = 4;
info.transpose = is_stride_1_dilation_1 && is_dot8_supported;
+ TensorInfo output_multipliers_shifts_info(TensorInfo(TensorShape(1U), 1, DataType::S32));
if(is_quantized)
{
- const UniformQuantizationInfo iq_info = input->quantization_info().uniform();
- const UniformQuantizationInfo wq_info = weights->quantization_info().uniform();
- const UniformQuantizationInfo oq_info = (output->total_size() == 0) ? iq_info : output->quantization_info().uniform();
+ if(is_data_type_quantized_per_channel(weights->data_type()))
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QSYMM8_PER_CHANNEL);
- const float multiplier = iq_info.scale * wq_info.scale / oq_info.scale;
- ARM_COMPUTE_UNUSED(multiplier);
- ARM_COMPUTE_RETURN_ERROR_ON(multiplier > 1.0f);
+ const size_t idx_c = get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::CHANNEL);
+ output_multipliers_shifts_info.set_tensor_shape(TensorShape(weights->dimension(idx_c)));
+ }
+ else
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
+ }
}
if(needs_permute)
@@ -83,25 +89,29 @@
const TensorInfo permuted_weights = weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_weights_shape).set_data_layout(DataLayout::NCHW);
const TensorInfo permuted_output = output->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_output_shape).set_data_layout(DataLayout::NCHW);
- ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayer3x3NCHWKernel::validate(&permuted_input, &permuted_weights, biases, &permuted_output, conv_info, depth_multiplier, act_info, gpu_target,
- dilation));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayer3x3NCHWKernel::validate(&permuted_input, &permuted_weights, biases, &permuted_output,
+ conv_info, depth_multiplier, act_info, gpu_target,
+ dilation, &output_multipliers_shifts_info, &output_multipliers_shifts_info));
}
else if(is_nhwc)
{
if(needs_weights_reshape)
{
auto reshaped_weights_shape = arm_compute::misc::shape_calculator::compute_reshaped_depthwise_weights_shape(*weights, info);
- ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayer3x3NHWCKernel::validate(input, &weights->clone()->set_tensor_shape(reshaped_weights_shape), biases, output, conv_info, depth_multiplier,
- act_info, dilation));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayer3x3NHWCKernel::validate(input, &weights->clone()->set_tensor_shape(reshaped_weights_shape), biases,
+ output, conv_info, depth_multiplier, act_info,
+ dilation, &output_multipliers_shifts_info, &output_multipliers_shifts_info));
}
else
{
- ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayer3x3NHWCKernel::validate(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayer3x3NHWCKernel::validate(input, weights, biases, output, conv_info, depth_multiplier, act_info,
+ dilation, &output_multipliers_shifts_info, &output_multipliers_shifts_info));
}
}
else
{
- ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayer3x3NCHWKernel::validate(input, weights, biases, output, conv_info, depth_multiplier, act_info, gpu_target, dilation));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayer3x3NCHWKernel::validate(input, weights, biases, output, conv_info, depth_multiplier, act_info, gpu_target,
+ dilation, &output_multipliers_shifts_info, &output_multipliers_shifts_info));
}
return Status{};
}
@@ -143,9 +153,14 @@
_permuted_input(),
_permuted_weights(),
_permuted_output(),
+ _output_multipliers(),
+ _output_shifts(),
_original_weights(),
+ _input(),
+ _output(),
_needs_permute(false),
- _is_prepared(false)
+ _is_prepared(false),
+ _is_quantized(false)
{
}
@@ -162,8 +177,11 @@
act_info,
dilation));
+ _is_quantized = is_data_type_quantized(input->info()->data_type());
_is_prepared = false;
_original_weights = weights;
+ _input = input;
+ _output = output;
_needs_permute = input->info()->data_layout() == DataLayout::NCHW;
ICLTensor *input_to_use = input;
@@ -190,11 +208,27 @@
output_to_use = &_permuted_output;
}
+ CLTensor *output_multipliers_to_use = nullptr;
+ CLTensor *output_shifts_to_use = nullptr;
+ if(_is_quantized)
+ {
+ const size_t idx_c = get_data_layout_dimension_index(weights->info()->data_layout(), DataLayoutDimension::CHANNEL);
+ const size_t num_filters = (is_data_type_quantized_per_channel(weights->info()->data_type())) ? weights->info()->dimension(idx_c) : 1;
+
+ _output_multipliers.allocator()->init(TensorInfo(TensorShape(num_filters), 1, DataType::S32));
+ _output_shifts.allocator()->init(TensorInfo(TensorShape(num_filters), 1, DataType::S32));
+
+ output_multipliers_to_use = &_output_multipliers;
+ output_shifts_to_use = &_output_shifts;
+ }
+
DWCWeightsKernelInfo dwc_weights_info;
dwc_weights_info.n0 = (depth_multiplier == 1) ? 8 : 1;
DWCKernelInfo dwc_info;
dwc_info.activation_info = act_info;
- _dwc_native_kernel.configure(input_to_use, weights_to_use, biases, output_to_use, dwc_weights_info, dwc_info, conv_info, depth_multiplier, dilation);
+ _dwc_native_kernel.configure(input_to_use, weights_to_use, biases, output_to_use,
+ dwc_weights_info, dwc_info, conv_info, depth_multiplier, dilation,
+ output_multipliers_to_use, output_shifts_to_use);
if(_needs_permute)
{
@@ -205,6 +239,12 @@
_permute_output_to_nchw.configure(&_permuted_output, output, PermutationVector(1U, 2U, 0U));
_permuted_output.allocator()->allocate();
}
+
+ if(_is_quantized)
+ {
+ _output_multipliers.allocator()->allocate();
+ _output_shifts.allocator()->allocate();
+ }
}
Status CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayerGeneric::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output,
@@ -225,6 +265,24 @@
const bool needs_permute = input->data_layout() == DataLayout::NCHW;
+ const bool is_quantized = is_data_type_quantized(input->data_type());
+
+ TensorInfo output_multipliers_shifts_info(TensorInfo(TensorShape(1U), 1, DataType::S32));
+ if(is_quantized)
+ {
+ if(is_data_type_quantized_per_channel(weights->data_type()))
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QSYMM8_PER_CHANNEL);
+
+ const size_t idx_c = get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::CHANNEL);
+ output_multipliers_shifts_info.set_tensor_shape(TensorShape(weights->dimension(idx_c)));
+ }
+ else
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
+ }
+ }
+
if(needs_permute)
{
TensorShape permuted_input_shape = input->tensor_shape();
@@ -242,12 +300,14 @@
ARM_COMPUTE_RETURN_ON_ERROR(CLPermute::validate(input, &permuted_input, PermutationVector(2U, 0U, 1U)));
ARM_COMPUTE_RETURN_ON_ERROR(CLPermute::validate(weights, &permuted_weights, PermutationVector(2U, 0U, 1U)));
ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayerNativeKernel::validate(&permuted_input, &permuted_weights, biases, &permuted_output, dwc_weights_info,
- dwc_info, conv_info, depth_multiplier, dilation));
+ dwc_info, conv_info, depth_multiplier, dilation,
+ &output_multipliers_shifts_info, &output_multipliers_shifts_info));
ARM_COMPUTE_RETURN_ON_ERROR(CLPermute::validate(&permuted_output, output, PermutationVector(1U, 2U, 0U)));
}
else
{
- ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayerNativeKernel::validate(input, weights, biases, output, dwc_weights_info, dwc_info, conv_info, depth_multiplier, dilation));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayerNativeKernel::validate(input, weights, biases, output, dwc_weights_info, dwc_info, conv_info, depth_multiplier,
+ dilation, &output_multipliers_shifts_info, &output_multipliers_shifts_info));
}
return Status{};
}
@@ -273,6 +333,19 @@
{
if(!_is_prepared)
{
+ if(_is_quantized)
+ {
+ _output_multipliers.map();
+ _output_shifts.map();
+ quantization::compute_quantized_multipliers_and_shifts(_input,
+ _original_weights,
+ _output,
+ reinterpret_cast<int32_t *>(_output_multipliers.ptr_to_element(Coordinates(0))),
+ reinterpret_cast<int32_t *>(_output_shifts.ptr_to_element(Coordinates(0))));
+ _output_multipliers.unmap();
+ _output_shifts.unmap();
+ }
+
if(_needs_permute)
{
ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());
@@ -286,40 +359,63 @@
}
CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayerInternal3x3::CLDepthwiseConvolutionLayerInternal3x3(std::shared_ptr<IMemoryManager> memory_manager)
- : _memory_group(std::move(memory_manager)), _kernel(nullptr), _border_handler(), _permute_input_to_nchw(), _permute_weights_to_nchw(), _permute_output_to_nhwc(), _reshape_weights(), _permuted_input(),
- _permuted_weights(), _permuted_output(), _original_weights(nullptr), _needs_permute(false), _needs_weights_reshape(false), _is_prepared(false)
+ : _memory_group(std::move(memory_manager)),
+ _kernel(nullptr),
+ _border_handler(),
+ _permute_input_to_nchw(),
+ _permute_weights_to_nchw(),
+ _permute_output_to_nhwc(),
+ _reshape_weights(),
+ _permuted_input(),
+ _permuted_weights(),
+ _permuted_output(),
+ _output_multipliers(),
+ _output_shifts(),
+ _original_weights(nullptr),
+ _input(nullptr),
+ _output(nullptr),
+ _needs_permute(false),
+ _needs_weights_reshape(false),
+ _is_prepared(false),
+ _is_quantized(false)
{
}
void CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayerInternal3x3::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output,
const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation)
{
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
- // idx_w and idx_h only used for validation
- const size_t idx_w = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::WIDTH);
- const size_t idx_h = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT);
- ARM_COMPUTE_UNUSED(idx_w);
- ARM_COMPUTE_UNUSED(idx_h);
+ const GPUTarget gpu_target = CLScheduler::get().target();
- ARM_COMPUTE_ERROR_ON(weights->info()->dimension(idx_w) + (weights->info()->dimension(idx_w) - 1) * (dilation.x() - 1) > input->info()->dimension(idx_w) + conv_info.pad_left() + conv_info.pad_right());
- ARM_COMPUTE_ERROR_ON(weights->info()->dimension(idx_h) + (weights->info()->dimension(idx_h) - 1) * (dilation.y() - 1) > input->info()->dimension(idx_h) + conv_info.pad_top() + conv_info.pad_bottom());
+ // Perform validation step
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
+ ARM_COMPUTE_ERROR_THROW_ON(CLDepthwiseConvolutionLayer3x3::validate(input->info(),
+ weights->info(),
+ biases != nullptr ? biases->info() : nullptr,
+ output->info(),
+ conv_info,
+ depth_multiplier,
+ act_info,
+ gpu_target,
+ dilation));
- const bool is_nhwc = input->info()->data_layout() == DataLayout::NHWC;
-
+ const bool is_nhwc = input->info()->data_layout() == DataLayout::NHWC;
+ _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type());
_needs_permute = is_nhwc && (depth_multiplier > 1);
- _needs_weights_reshape = is_nhwc && (depth_multiplier == 1)
- && is_data_type_quantized_asymmetric(input->info()->data_type());
+ _needs_weights_reshape = is_nhwc && (depth_multiplier == 1) && _is_quantized;
+
_is_prepared = false;
_original_weights = weights;
+ _input = input;
+ _output = output;
ICLTensor *input_to_use = input;
const ICLTensor *weights_to_use = weights;
ICLTensor *output_to_use = output;
- const bool is_stride_1 = ((conv_info.stride().first == conv_info.stride().second) && (conv_info.stride().first == 1));
- const bool is_dot8_supported = dot8_supported(CLKernelLibrary::get().get_device());
- const bool is_stride_1_dilation_1 = (is_stride_1 && dilation.x() == 1 && dilation.y() == 1);
+ const bool is_quantized_per_channel = is_data_type_quantized_per_channel(weights->info()->data_type());
+ const bool is_stride_1 = ((conv_info.stride().first == conv_info.stride().second) && (conv_info.stride().first == 1));
+ const bool is_dot8_supported = dot8_supported(CLKernelLibrary::get().get_device()) && !is_quantized_per_channel;
+ const bool is_stride_1_dilation_1 = (is_stride_1 && dilation.x() == 1 && dilation.y() == 1);
DepthwiseConvolutionReshapeInfo info;
info.c0 = 4;
@@ -359,9 +455,30 @@
_kernel = arm_compute::support::cpp14::make_unique<CLDepthwiseConvolutionLayer3x3NCHWKernel>();
}
+ CLTensor *output_multipliers_to_use = nullptr;
+ CLTensor *output_shifts_to_use = nullptr;
+ if(_is_quantized)
+ {
+ const size_t idx_c = get_data_layout_dimension_index(weights->info()->data_layout(), DataLayoutDimension::CHANNEL);
+ const size_t num_filters = (is_quantized_per_channel) ? weights->info()->dimension(idx_c) : 1;
+
+ _output_multipliers.allocator()->init(TensorInfo(TensorShape(num_filters), 1, DataType::S32));
+ _output_shifts.allocator()->init(TensorInfo(TensorShape(num_filters), 1, DataType::S32));
+
+ output_multipliers_to_use = &_output_multipliers;
+ output_shifts_to_use = &_output_shifts;
+ }
+
// Configure kernel
- _kernel->set_target(CLScheduler::get().target());
- _kernel->configure(input_to_use, weights_to_use, biases, output_to_use, conv_info, depth_multiplier, act_info, dilation);
+ _kernel->set_target(gpu_target);
+ _kernel->configure(input_to_use, weights_to_use, biases, output_to_use, conv_info, depth_multiplier,
+ act_info, dilation, output_multipliers_to_use, output_shifts_to_use);
+
+ if(_is_quantized)
+ {
+ _output_multipliers.allocator()->allocate();
+ _output_shifts.allocator()->allocate();
+ }
// Permute output if needed
if(_needs_permute)
@@ -412,6 +529,19 @@
{
if(!_is_prepared)
{
+ if(_is_quantized)
+ {
+ _output_multipliers.map();
+ _output_shifts.map();
+ quantization::compute_quantized_multipliers_and_shifts(_input,
+ _original_weights,
+ _output,
+ reinterpret_cast<int32_t *>(_output_multipliers.ptr_to_element(Coordinates(0))),
+ reinterpret_cast<int32_t *>(_output_shifts.ptr_to_element(Coordinates(0))));
+ _output_multipliers.unmap();
+ _output_shifts.unmap();
+ }
+
if(_needs_permute)
{
ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());