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path: root/src/cpu/operators/CpuGemmConv2d.cpp
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Diffstat (limited to 'src/cpu/operators/CpuGemmConv2d.cpp')
-rw-r--r--src/cpu/operators/CpuGemmConv2d.cpp73
1 files changed, 44 insertions, 29 deletions
diff --git a/src/cpu/operators/CpuGemmConv2d.cpp b/src/cpu/operators/CpuGemmConv2d.cpp
index ebf2ebcc1b..7c0e58b94e 100644
--- a/src/cpu/operators/CpuGemmConv2d.cpp
+++ b/src/cpu/operators/CpuGemmConv2d.cpp
@@ -62,13 +62,13 @@ CpuGemmConv2d::SkipInfo CpuGemmConv2d::skip_im_col_info(const ITensorInfo *src,
const unsigned int kernel_height = weights->dimension(idx_height);
unsigned int conv_w = 0;
unsigned int conv_h = 0;
- std::tie(conv_w, conv_h) = scaled_dimensions(src->dimension(idx_width),
- src->dimension(idx_height),
- kernel_width,
- kernel_height,
- conv_info,
- dilation);
- const bool skip_im2col = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv_info.stride().first == 1 && conv_info.stride().second == 1);
+ std::tie(conv_w, conv_h) = scaled_dimensions(src->dimension(idx_width),
+ src->dimension(idx_height),
+ kernel_width,
+ kernel_height,
+ conv_info,
+ dilation);
+ const bool skip_im2col = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv_info.stride().first == 1 && conv_info.stride().second == 1);
if(skip_im2col)
{
@@ -139,8 +139,8 @@ void CpuGemmConv2d::configure_mm(const ITensorInfo *src, const ITensorInfo *weig
PixelValue type_min{};
PixelValue type_max{};
std::tie(type_min, type_max) = get_min_max(data_type);
- int32_t min_activation = type_min.get<int32_t>();
- int32_t max_activation = type_max.get<int32_t>();
+ int32_t min_activation = type_min.get<int32_t>();
+ int32_t max_activation = type_max.get<int32_t>();
if(supported_acts.count(act_info.activation()) != 0)
{
@@ -203,8 +203,8 @@ Status CpuGemmConv2d::validate_mm(const ITensorInfo *src, const ITensorInfo *wei
PixelValue type_min{};
PixelValue type_max{};
std::tie(type_min, type_max) = get_min_max(data_type);
- int32_t min_activation = type_min.get<int32_t>();
- int32_t max_activation = type_max.get<int32_t>();
+ int32_t min_activation = type_min.get<int32_t>();
+ int32_t max_activation = type_max.get<int32_t>();
const std::set<ActivationLayerInfo::ActivationFunction> supported_acts = { ActivationLayerInfo::ActivationFunction::RELU,
ActivationLayerInfo::ActivationFunction::BOUNDED_RELU,
@@ -274,6 +274,7 @@ void CpuGemmConv2d::configure(const ITensorInfo *src, const ITensorInfo *weights
const DataLayout data_layout = src->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);
const int idx_kernels = get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES);
const unsigned int kernel_width = weights->dimension(idx_width);
@@ -288,8 +289,8 @@ void CpuGemmConv2d::configure(const ITensorInfo *src, const ITensorInfo *weights
ITensorInfo *gemm_output_to_use = dst;
// Get convolved dimensions
- unsigned int conv_w = 0;
- unsigned int conv_h = 0;
+ unsigned int conv_w = 0;
+ unsigned int conv_h = 0;
std::tie(conv_w, conv_h) = scaled_dimensions(src->dimension(idx_width),
src->dimension(idx_height),
kernel_width,
@@ -306,8 +307,8 @@ void CpuGemmConv2d::configure(const ITensorInfo *src, const ITensorInfo *weights
_skip_col2im = skip_info.skip_col2im;
// Get parameters from conv_info
- unsigned int stride_x = 0;
- unsigned int stride_y = 0;
+ unsigned int stride_x = 0;
+ unsigned int stride_y = 0;
std::tie(stride_x, stride_y) = conv_info.stride();
unsigned int mat_weights_cols = weights->dimension(idx_kernels);
@@ -321,9 +322,15 @@ void CpuGemmConv2d::configure(const ITensorInfo *src, const ITensorInfo *weights
// Create tensor to store im2col reshaped inputs
if(!_skip_im2col)
{
+ const int block_by = arm_compute::block_by(weights_info.weight_format());
+ unsigned int input_pad_right = 0;
+ if(block_by > 1)
+ {
+ input_pad_right = (src->dimension(idx_channel) % block_by) == 0 ? 0 : block_by - (src->dimension(idx_channel) % block_by);
+ }
// Configure
_im2col_kernel = std::make_unique<kernels::CpuIm2ColKernel>();
- _im2col_kernel->configure(src, &_im2col_output, Size2D(kernel_width, kernel_height), conv_info, false, dilation);
+ _im2col_kernel->configure(src, &_im2col_output, Size2D(kernel_width, kernel_height), conv_info, false, dilation, num_groups, input_pad_right);
// Update GEMM input
gemm_input_to_use = &_im2col_output;
@@ -399,12 +406,12 @@ Status CpuGemmConv2d::has_opt_impl(arm_compute::WeightFormat &expected_weight_fo
const unsigned int kernel_height = weights->dimension(idx_height);
unsigned int conv_w = 0;
unsigned int conv_h = 0;
- std::tie(conv_w, conv_h) = scaled_dimensions(src->dimension(idx_width),
- src->dimension(idx_height),
- kernel_width,
- kernel_height,
- conv_info,
- dilation);
+ std::tie(conv_w, conv_h) = scaled_dimensions(src->dimension(idx_width),
+ src->dimension(idx_height),
+ kernel_width,
+ kernel_height,
+ conv_info,
+ dilation);
const CpuGemmConv2d::SkipInfo skip_info = CpuGemmConv2d::skip_im_col_info(src, weights, conv_info,
dilation, act_info);
@@ -428,7 +435,7 @@ Status CpuGemmConv2d::validate(const ITensorInfo *src, const ITensorInfo *weight
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::BFLOAT16, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QSYMM8_PER_CHANNEL, DataType::BFLOAT16, DataType::F16, DataType::F32);
- if (!is_fixed_format(weights_info.weight_format()))
+ if(!is_fixed_format(weights_info.weight_format()))
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(src, weights);
}
@@ -469,9 +476,9 @@ Status CpuGemmConv2d::validate(const ITensorInfo *src, const ITensorInfo *weight
dilation);
// Check if GEMM3D is supported
- const CpuGemmConv2d::SkipInfo skip_info = CpuGemmConv2d::skip_im_col_info(src, weights, conv_info,
- dilation, act_info);
- const bool skip_im2col = skip_info.skip_im2col, skip_col2im = skip_info.skip_col2im;
+ const CpuGemmConv2d::SkipInfo skip_info = CpuGemmConv2d::skip_im_col_info(src, weights, conv_info,
+ dilation, act_info);
+ const bool skip_im2col = skip_info.skip_im2col, skip_col2im = skip_info.skip_col2im;
ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_channel) != src->dimension(idx_channel));
ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
@@ -504,6 +511,14 @@ Status CpuGemmConv2d::validate(const ITensorInfo *src, const ITensorInfo *weight
if(!skip_im2col)
{
+ const int block_by = arm_compute::block_by(weights_info.weight_format());
+ int input_pad_right = 0;
+ if(block_by > 1)
+ {
+ input_pad_right = (src->dimension(idx_channel) % block_by) == 0 ? 0 : block_by - (src->dimension(idx_channel) % block_by);
+ mat_weights_rows = weights->dimension(idx_width) * weights->dimension(idx_height) * (weights->dimension(idx_channel) + input_pad_right);
+ }
+
// Create tensor info for im2col reshaped inputs
// For CPU, the batch size is on the fourth dimension
TensorShape shape_im2col = src->tensor_shape();
@@ -513,7 +528,7 @@ Status CpuGemmConv2d::validate(const ITensorInfo *src, const ITensorInfo *weight
im2col_reshaped_info = TensorInfo(shape_im2col, 1, data_type);
im2col_reshaped_info.set_quantization_info(src->quantization_info());
- ARM_COMPUTE_RETURN_ON_ERROR(kernels::CpuIm2ColKernel::validate(src, &im2col_reshaped_info, Size2D(kernel_width, kernel_height), conv_info, append_bias, dilation, 1));
+ ARM_COMPUTE_RETURN_ON_ERROR(kernels::CpuIm2ColKernel::validate(src, &im2col_reshaped_info, Size2D(kernel_width, kernel_height), conv_info, append_bias, dilation, num_groups, input_pad_right));
gemm_input_to_use = &im2col_reshaped_info;
}
@@ -563,7 +578,7 @@ void CpuGemmConv2d::run(ITensorPack &tensors)
{
// Run input reshaping
unsigned int y_dim = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT);
- ITensorPack pack =
+ ITensorPack pack =
{
{ TensorType::ACL_SRC, src },
{ TensorType::ACL_DST, im2col_output.get() }
@@ -657,7 +672,7 @@ void CpuGemmConv2d::prepare(ITensorPack &tensors)
// Run weights reshaping and mark original weights tensor as unused
CpuAuxTensorHandler weights_reshaped(offset_int_vec(WeightsReshaped), _weights_reshaped, tensors);
auto weights = tensors.get_const_tensor(TensorType::ACL_SRC_1);
- ITensorPack pack =
+ ITensorPack pack =
{
{ TensorType::ACL_SRC, weights },
{ TensorType::ACL_DST, weights_reshaped.get() }
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-23 19:46:59 +0000